Skoltech is an international graduate research-focused university that was founded by the group of world-renowned scientists in 2011. Skoltech's curriculum focuses on technology and innovation, offering Master's programs in 11 technological disciplines. Students receive rigorous theoretical and practical training, design their own research projects, participate in internships and gain entrepreneurial skills in English. The faculty is comprised of current researchers with international accreditation and achievements.
Course Catalog for 2021-2022
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Term 1
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
|---|---|---|---|---|---|
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Academic Communication: Preparatory English for Phd Exam (Term 1-2)
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. The course is aligned with the NATURE MASTERCLASS available to Skoltech researchers, so you will be able to benefit from professional recommendations of the Nature experts regarding the structure and contents of a publication, and constructive feedback from your Instructor on the language of your materials. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities, to be performed in class and at home, individually and in teams. By the end of the course, successful participants will – know the rules and conventions of research paper writing, including structure, style, grammar and vocabulary; – improve their academic communication skills, such as active listening, spontaneous and rehearsed speaking/ presentation, reading and writing within a given academic genre; – have experience in writing, editing, peer-reviewing and presenting research results. |
Elizaveta Tikhomirova |
31.5 per term
|
Extra | DF030029 | |
|
Advanced PLM Techniques: Testing and Models Validation
This course is final course in PLM series and is devoted to the different types of testing and numerical models validation.
Students learn how to perform vibrational and modal testing in order to identify dynamic parameters of a given structure. The modal testing is performed using laser scanning vibrometry. The results of modal and vibrational testing are used for finite-element model validation and updating for accurate dynamics simulation. Also, so called Hardware-in-the-Loop (HiL) testing is an important part of the course. The idea of HiL is to upload the functional model of the investigated system to real-time board and test it in combination with physical parts. During the course students perform a number of tests with the system that was designed and prototyped during courses Advanced PLM I and Advanced PLM II. Finally, the results are used for system model validation. |
Ighor Uzhinsky | 6 | MA060254 | CANCELLED | |
|
Advanced Quantum Mechanics
Lecture Course “Advanced Quantum Mechanics” comprises a number of topics which are not included in standard courses on Quantum Mechanics. Meanwhile, these topics acquire increasing importance during last 2-3 decades due to developing applications in various branches of quantum condensed-matter physics theory. The first set of topics refers to examples of adiabatic or weakly non-adiabatic behavior of quantum system: Berry phases and Landau-Zener tunneling. Secondly, we discuss Feynman path integral representation of quantum mechanics. The third part of the course is devoted to the density matrix formalism for description of open systems.
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Konstantin Tikhonov | 3 | DA030207 | CANCELLED | |
|
Advanced Quantum Mechanics
Lecture Course “Advanced Quantum Mechanics” comprises a number of topics which are not included in standard courses on Quantum Mechanics. Meanwhile, these topics acquire increasing importance during last 2-3 decades due to developing applications in various branches of quantum condensed-matter physics theory. The first set of topics refers to examples of adiabatic or weakly non-adiabatic behavior of quantum system: Berry phases and Landau-Zener tunneling. Secondly, we discuss Feynman path integral representation of quantum mechanics. The third part of the course is devoted to the density matrix formalism for description of open systems.
|
Konstantin Tikhonov | 3 | DA030207 | CANCELLED | |
|
Advanced Reservoir Modeling and Engineering
This course is is covered base and advanced theory and practice of reservoir engineering aimed to merge development, technological and modeling parts that leads to digital oilfield. In addition, main part of this course is practical hydrodynamics modeling of reservoir development:
1) Modern physics of oil and gas displacement/hydrodynamics, stages of reservoir development, well drilling and problems during reservoir development. 2) Russian and international ways of oil and gas reserves calculation, required technical documentation of reservoir development plan. 3) Advanced of hydrodynamic modeling, including theory of hydrodynamic, practical course of modern hydrodynamic simulators (CMG Stars/GEM/IMEX – for EOR modeling, including thermal and chemical methods, RFD tNavigator – as modern Russian simulator, which allow to fast solving of practical tasks) 4) Expertise of hydrodynamic models (input information, quality of model building, quality of matching and forecast results). 5) Complications during lift of oil and gas through production wells and pipeline systems and methods of prevention. 6) Digital filed technology – optimization tasks, management of reservoir development and of virtual oil and gas flow measurement methods. 7) Structure of modern oil and gas companies, data-flow inside. Results of this course: |
Alexander Cheremisin | 3 | MA030468 | ||
|
Advanced Solvers for Numerical PDEs
Partial differential equations (PDEs) describe many phenomena around us: from tsunami propagation to neural currents in the human head. Given today’s accuracy demands in industrial and academic applications, numerical solution of the PDEs typically involves millions and even billions of unknowns. This course presents and analyses efficient algorithms for their fast and economical solution.
We recall modern iterative solvers (Krylov subspace methods) and discuss robust economical preconditioners (Krylov subspace methods) and robust economical preconditioners (FFT-related, geometric and algebraic multigrid, and others) for the arising large discrete problems. We also look at the possibilities of machine learning and dimensionality reduction methods for numerical solution of the PDEs. We will concentrate on diffusive, time-harmonic electromagnetic and acoustic systems and models of fluid dynamics. Special emphasis will be on the design of the solvers for anisotropic and/or highly-heterogeneous problems, problems on nonuniform and unstructured computational grids. The homogenization technique as a means to extract homogeneous effective parameters from disordered or heterogeneous media will be discussed. Numerical modeling with both standard (finite-difference) and advanced discretization methods will be considered (finite element, mixed finite element, and discontinuous Galerkin). This course is intended to attract student of all majors interested |
Nikolay Yavich | 3 | MA030470 | ||
|
Affine Quantum Groups (Term 1B-2)
The main source of the theory of quantum groups was integrable models. Affine quantum groups (better to say quantum universal enveloping of affine Lie algebras) are symmetries of the XXZ model, they appeared simultaneously with quantum group theory in the mid-80s. Affine quantum groups have many new properties (compared to quantum universal enveloping algebras of simple Lie algebras) — different realizations, different comultiplications. They have many applications, in addition to the integrable models mentioned above, we mention cluster algebras and geometric representation theory.
In the course, we will discuss the basic constructions associated with quantum affine algebras and touch on their applications. Some basic familiarity with quantum groups and affine Lie algebras is assumed, and familiarity with the Bethe ansatz is also desirable. |
Mikhail Bershtein |
63 per term
|
MA060462 | ||
|
Applied Materials and Design (Term 5B-6)
This course provides a broad base introduction into materials science and engineering of applied materials. The fundamental physical phenomena are considered that occur at different scales in the main classes of applied materials: metals, ceramics, polymers, natural materials, composites, and hybrids. The interrelation between thermodynamics, diffusion kinetics, and deformation behavior is explored. The concept of structure is introduced, and the nature of structural elements at the atomic, molecular, nano-, micrometer and macroscopic scales is discussed: short and long range order in amorphous materials and crystals, defects, crystallites, grains and subgrains, precipitates, grain boundaries, interfaces, spherulites, etc. These are used to demonstrate the principal approaches to property control and evaluation in materials engineering and related technologies: chemical composition, synthesis, fabrication, heat treatment, plastic deformation, hybridization, and surface engineering.
Principles to control the properties are translated in terms of design performance. Ashby’s material selection algorithm for rational selection of materials for specific designs and applications will be taught here in comprehensive way – analysis of function, objectives and constraints, deducing of performance indices. All the concepts covered in lectures will be practiced by using CES EduPack a software to implement data intensive learning. The lectures will be supported with a number of laboratory practical lessons devoted to the development of practical skills in traditional materials science research flow – the visualization, characterization and modification of structure followed by the testing and analysis of properties. All the concepts covered in lectures will be the subject of exercises using open source software to implement data intensive learning. Individual projects (problems) will be formulated to introduce the CDIO approach in Applied Materials and Design. |
Alexander Korsunsky |
63 per term
|
MA060431 | CANCELLED | |
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Basic Molecular Biology Techniques
The purpose of this course is to provide students with the opportunity to obtain and develop the basic set of skills needed to be successful in a molecular biology laboratory. The course consists of hands-on laboratory work, as well as lectures from course instructors. Students without any significant background in the biological sciences should be advised that additional reading outside of the scheduled classes may be necessary to maximize classroom success (instructors are happy to provide resources at the students’ request).
|
Svetlana Dubiley | 6 | MA060022 | ||
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Bayesian Methods of Machine Learning (Term 1B-2)
The course addresses Bayesian approach for solving various machine learning and data analysis problems. It offers the framework for solving inverse problems, model selection and continual learning. The most attention is payed to the fusion between the deep learning techniques and the elements of bayesian approach. This fusion is possible due to the development of a range of approximate inference techniques in the last 20 years.
Modern machine learning papers frequently use machinery of the approximate inference techniques. The main learning outcome of the course is the ability to read and reproduce related papers, and to apply corresponding methods of approximate inference for the development of Bayesian machine learning approaches. In order to reach this goal, the course contains theoretical and practical assignments and the final project. The purpose of the theoretical tasks is two-fold. Firstly, we would like to develop skills of equation derivation, that is used routinely in papers and is usually suppressed. Secondly, some theoretical tasks provide the intuition about properties of the methods through toy models. The purpose of the practical tasks is straightforward: to translate discussed methods to the code and observe properties of the methods through examples. These assignments leads to the course projects: reproducing and discussing a relevant paper from a recent conference, and/or development of some students' research ideas using methods of Bayesian machine learning. |
Evgeny Burnaev |
63 per term
|
MA060129 | MOVED FROM T5 | |
|
Biomedical Mass Spectrometry (Term 5B-6)
This course introduces students to the first principles and methods of mass spectrometry with special emphasize on biological and medical applications. The course will cover wide range of mass spectrometry techniques used for ion generation, separation, detection and data processing and interpretation. The course will teach the theoretical fundamentals required for the design of instruments and methods for measuring mass spectra of biological samples. The course will cover mass spectrometry applications in OMICs technologies, mass spectrometry applications in biomarker discovery and tissue imaging.
After successful completion of this class, students will acquire the initial knowledge of the operational principles and design of different mass spectrometers, different methods of ionization of biological molecules of wide mass range, different methods of ion separation including magnetic sector, time of flight, RF and DC ion traps, as well as FTICR. Experimental and bioinformatics based methods of protein, peptides, lipids and metabolite molecule identification, different fragmentation methods for primary and secondary structure determination, methods of quantitative determination of proteins, lipids, metabolites and small molecule in physiological liquids |
Evgeny Nikolaev |
63 per term
|
MA060256 | MOVED FROM T5-6 | |
|
Classical Integrable Systems (Term 5-6)
Course description: A self-contained introduction to the theory of soliton equations with an emphasis on their algebraic-geometrical integration theory. Topics include:
1. General features of the soliton systems. 2. Algebraic-geometrical integration theory. 3. Hamiltonian theory of soliton equations. 4. Perturbation theory of soliton equations and its applications to Topological Quantum |
Igor Krichever |
63 per term
|
DA060179 | ||
|
Complex Networks
The aim of the course is to overview network science, and to explain basic ideas and tools used in analyzing large networks. Many modern networks are rapidly changing and growing, and the course emphasizes techniques suited to such classes of networks. We will start with networks growing via the addition of edges and show that at any instance they can be interpreted statically as random graphs. Other classes of growing networks are genuinely dynamicал. We will analyze the re-direction algorithm that is very efficient in generating rather realistic networks. The power of the choice algorithm, as well as the redirection algorithm, will be also covered.
|
Pavel Krapivsky | 3 | MA030469 | ||
|
Computational Imaging
In the computational era of everything, imaging has not become an exception. Computational algorithms allow both to extract valuable information from a scene and to improve the very sensor that forms the image. Today, computational and image processing enhancements became integrable parts of any digital imager, be it a miniature smartphone camera or a complex space telescope.
This crash course is designed as a prerequisite for those students who would like to venture into the field of Computer Vision. We will cover foundational mathematical equations that are involved in the image formation and in the geometric projection principles. The concept of Point Spread Function that distorts the object will be explained on particular examples and will be experimented with for the tasks of image reconstruction and denoising. Image processing will be covered with an emphasis on the Python libraries to be used in the rest of the imaging-related courses on the DS/IST tracks (openCV and others). A basic DSLR photo camera will be considered as a model for understanding Fourier Imaging and Filtering methods in a laboratory exercise. Hands-on tutorials on how to select a camera and a lens for your machine vision application will be provided. The theory of color and stereo light-field cameras will be covered using the models of commonplace Bayern RGB sensors; as well as state-of-art spectral and multi-lens imagers. The course will consist of three theoretical lectures riffled by three graded in-class laboratory coding sessions on the subjects covered in the theoretical lectures. 100% attendance is mandatory. There will be a single in-class exam during the evaluation week and no homework. |
Dmitry Dylov | 3 | MA030121 | ||
|
Differential Geometry of Connections (Term 1B-2)
In this course we present the basic concepts of modern differential geometry: metric, curvature, connection, etc. The goal of our study is to develop tools for practical efficient computations (including the art of manipulation with indices) supported by a deeper understanding of the geometric meaning of all notions and theorems. We will develop an approach based on the notion of connection with all its different aspects: covariant derivative, parallel transport, collection of Christoffel symbols, matrix-valued one-form etc.
|
Maxim Kazarian |
63 per term
|
MA060460 | ||
|
Energy Conversion Systems Optimal Management and Integration
This course will provide a graduate level overview of modern energy conversion systems ranging from state of the art commercial technologies, real world ones, generating electric/mechanic, heating and cooling power, as well as innovative solutions yet to be deployed massively, e.g. Flow Batteries, Power-to-Gas, Carbon Capture, Storage and Utilization (CCSU). During the course, the energy conversion units will be always studied from an integrated point of view considering their interaction with the surrounding energy infrastructures, electric, thermal/cooling and gas networks requirements, as well as the loads to be fulfilled. The whole course will look at the units from their optimal management point of view within such integrated framework, identifying the more suitable way to characterize their performance (e.g., constant, linear, non-linear) consistently with the objectives, economic and/or environmental one. Such task is continuously increasing in complexity due to the uncontrollable Renewable Energy Sources increasing deployment, therefore units are facing technological developments, increasing their flexibility, and the energy infrastructures are increasing their level of mutual integration. Overall learning how to assess the adopted solutions is always more important, thus the core of the course is indeed represented by the identification of an integration problem, which will be assessed via the development of an optimization model to utilize assessing the validity of the investigated solution, both in economic terms as well as in environmental (primary energy consumption/CO2 emissions) on a research based teaching fashion.
|
Aldo Bischi | 3 | MA030395 | ||
|
English
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research and follows the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, last of three weeks and take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
|
Essential Engineering Toolbox
The course is a seriеs of tutorials on essential tools that are extensively used throughout coursework and research in the Advanced Manufacturing Technologies program and generally in engineering practice. The tutorials involve hands-on exercises on a computer.
The course introduces Latex, Python, Mathematica, and Matlab that are routinely used during research work. Each topic will consist of an introductory lecture followed by practical exercises. Students will be required to solve particular problems and write reports in Latex using the tools learned in the tutorials. |
Petr Zhilyaev | 3 | MA030351 | ||
|
Experimental Data Processing
The course introduces students to practically useful approaches of data processing for control and forecasting. The focus will be on identifying the hidden and implicit features and regularities of dynamical processes using experimental data. The course exposes data processing methods from multiple vantage points: standard data processing methods and their hidden capacity to solve difficult problems; statistical methods based on state-space models; methods of extracting the regularities of a process on the basis of identifying key parameters. The course addresses the problems in navigation, solar physics, geomagnetism, space weather and biomedical research and will be useful for a broad range of interdisciplinary applications.
|
Tatiana Podladchikova | 6 | MA060238 | ||
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Facilitating and Assessing Learning (Term 1B-2)
The course offers an introduction to facilitating learning in higher education for junior faculty together with PhD student TAs. The course content focuses on aligning learning outcomes with learning activities and assessment strategies. Constructive alignment in the course is defined at high resolution such that learning outcomes for a course are elaborated into separate activities and assignments for students. In other words, learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. The assessment design that participants in the course design will therefore be required to reflect significant and effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments, and the course therefore comes with an emphasis on communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course contains discussion topics to highlight ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching and course development or their supervisory activities. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson |
31.5 per term
|
DG030030 | ||
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Foundations of Software Engineering
This course is intended to serve as an introduction into basics of everyday industrial software engineering. Oftentimes students seek to obtain proficiency in complicated subjects such as machine learning, algorithms, or computer vision, but lack basic literacy in software engineering and therefore have little practical skills required to carry out research or industrial projects. In this course, our goal is to bridge the gap between basic programming skills commonly taught during BSc programs and the industrial-grade engineering required by top-notch MSc, PhD, or R&D positions.
Topics this year include: As a project, the students will be required to redesign, reengineer, refactor, test, and deploy an existing machine learning project using the principles described in this course. |
Alexey Artemov | 3 | MA030406 | ||
|
Geometric Representation Theory (Term 1B-2)
Geometric representation theory applies algebraic geometry to the problems of
representation theory. Some of the most famous problems of representation theory were solved on this way during the last 40 years. The list includes the Langlands reciprocity for the general linear groups over the functional fields, the Langlands-Shelstad fundamental Lemma, the proof of the Kazhdan-Lusztig conjectures; the computation of the characters of the finite groups of Lie type. We will study representations of the affine Hecke algebras using the geometry of affine Grassmannians (Satake isomorphism) and Steinberg varieties of triples (Deligne-Langlands conjecture). This is a course for master students knowing the basics of algebraic geometry, sheaf theory, homology and K-theory. |
Mikhail Finkelberg |
63 per term
|
DA060271 | ||
|
Innovation Workshop
The Innovation Workshop (IW) is a one-month full-time “boot camp” MS-level course that unites the entire Skoltech incoming class with faculty and esteemed invited mentors to create the foundational experience in Entrepreneurship and Innovation (E&I) for all. IW is designed to instill a positive “can-do” teamwork attitude in the Skoltech culture, as well as to cultivate the art of prototyping quickly, under pressure, with help from others, and based on whatever resources are at hand here and now.
. Experiential inquiry-based learning leads IW student through the entire technology innovation cycle along the three pillars of innovation: (i) Impact (Problem + Feedback), (ii) Novelty of the solution (IP + Prototype + Science), and (iii) Vision for the subsequent iterations (Next Steps + Picture of Success). This work is performed in cross-disciplinary teams operating under time pressure thus creating real life experience of complex innovation project. . This file is the abbreviated version of the IW syllabus that carries only the most technical summary information. Please find the full IW Syllabus in the Files section of your IW Canvas page, as well as the attachement to this submission (the clickable "Upload" URL in the bottom of this document). Students of the IW are strongly recommended to read the full Syllabus as it carries plenty of information necessary to succeed in the IW and in innovation in general. . |
Dmitry Kulish | 6 | E&I | MC060001 | |
|
Introduction to Advanced Manufacturing Technologies
The course provides an introduction to the field of Advanced Manufacturing Technologies and focuses on main research and educational thrusts of the Center for Design, Manufacturing and Materials (https://https://crei.skoltech.ru/cdmm): Advanced Manufacturing Technologies, Digital Engineering Technologies, Science of Advanced Manufacturing, and Computational Engineering.
The first thrust is focused on advanced technologies such as Advanced Manufacturing of Composite Materials, Additive Technologies, Thermal Spray Coatings, and Materials Selection in Design. The second thrust is focused on digital engineering technologies related to simulation-driven product development, model based systems engineering, digital manufacturing, product lifecycle management, and geometric modeling in Computer-Aided Design. The last two consist of fundamental disciplines required to understand the mechanics and physics of advance manufacturing processes, to develop mathematical and computational models of these processes, to predict and improve the properties of the materials, structures, and engineering systems, as well as to develop digital twins of manufacturing processes and their individual components, what is commonly referred to as simulation-based engineering science. Professors and research scientists from the Center for Design, Manufacturing and Materials will introduce students to the Center laboratories, to ongoing research activities, and propose possible projects for Master's thesis research. This course will help students to select a specialization and future research advisors. |
Alexander Safonov | 3 | MA030296 | ||
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Introduction to Artificial Intelligence
This is an introductory course which overviews general aspects of Artificial Intelligence such as main applications, ethics, current trends and challenges etc.
The course is aimed for 1st year MSc students who would like to become familiar with AI. Although the course does not go deeply into technical details of AI (which will be fought later on by other courses in the Data Science program), it will be also of interest to those who have experience in AI but would like to understand the general role the new AI technologies play in the modern society. During the course several topics will be discussed: |
Maxim Fedorov | 3 | MA030358 | CANCELLED | |
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Introduction to Data Science
The course gives an introduction to the main topics of modern data analysis such as classification, regression, clustering, dimensionality reduction, reinforcement and sequence learning, scalable algorithms. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The primary objective of the course is giving a broad overview of major machine learning techniques. Particular attention is paid to the modern data analysis libraries which allow solving efficiently the problems mentioned above.
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Maxim Panov, Mikhail Belyaev |
3 | MA030111 | ||
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Introduction to Life Sciences Program
This is an introductory course aiming to give the students coming into the Life Sciences Program a birds-eye view of the research directions in the field of biomedicine at Skoltech. Soon after coming to Skoltech, the incoming students will be expected to join a lab and pick a research project. The objective of this course is to make this decision more informed.
The course will be presented by Skoltech faculty involved in life sciences research, mainly those of the two centers, CLS, CNBR and DAL. These faculties are prospective research advisors for Skoltech LS students. The lectures will cover the context for the research, present the labs, and showcase the research projects performed in the labs. The final schedule (what lecture reads which professor, and when) is read by the end of October. |
Georgii Bazykin | 3 | MA030371 | ||
|
Introduction to Petroleum Engineering
The course is an introduction to Petroleum Engineering and gives an overview of Petroleum Engineering and its various components and their internal connection.
The course will address the story of oil from its origin to the end user. The objective is to provide an overview of the fundamental operations in exploration, drilling, production, processing, transportation, and refining of oil and gas. As additional topics it is planned to consider Permafrost Engineering and Flow Assurance, which are actual for Russian Oil&Gas Industry. Within the framework of the course it is planned to invite speakers from industry. |
Dimitri Pissarenko, Evgeny Chekhonin, Evgeny Chuvilin |
3 | MA030064 | ||
|
Introduction to Schramm-Loewner Evolution (Term 1B-2)
The Schramm-Loewner Evolution (SLE) was introduced in 1998 in order to describe all possible conformally invariant scaling limits that appear in many lattice models of statistical physics. Since then the subject has received a lot of attention and developed into a thriving area of research in its own right which has a lot of interesting connections with other areas of mathematics and physics. Beyond the aforementioned lattice models it is now related to many other areas including the theory of `loop soups', the Gaussian Free Field, and Liouville Quantum Gravity. The emphasis of the course will be on the basic properties of SLE and how SLE can be used to prove the existence of a conformally invariant scaling limit for lattice models.
Topics will include: |
Dmitry Belyaev |
63 per term
|
MA060494 | CANCELLED | |
|
Introduction to Wireless Communications
The course gives an introduction to the most important aspects of modern wireless communication systems. The course covers basic wireless communications processes like signal transmission, propagation, detection, and demodulation. Students will get familiar with some information-theoretic concepts like channel capacity and error-correcting codes. This introduction also highlights multiple antenna techniques and modern cellular system architectures, including the Internet-of-Things concepts. The practical part of the course includes a series of labs (MATLAB) that allow discovering basic principles as well as advanced methods for wireless communication systems analysis.
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Kirill Andreev | 3 | MA030409 | ||
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Leadership for Innovators
Successful innovators are distinguished not only by their scientific excellence as well as end user vision, but also by superior leadership skills. Scientists dream about being honored and awarded by fans on the merits of their science alone, but unfortunately it never works this way. Innovation is impossible without leading, cooperating, negotiating, and keeping resilience from the constant stress. This course presents the comprehensive skillset of leadership that includes theory and practice of: – leadership & teamwork – self-awareness and goal setting – stress management and self-presentation – empathy and 360 feedback – influence & negotiations The recurring topic of the class is that all these beneficial skills are fuzzy and overrated unless they are taken together in the globally accepted framework of "Emotional Intelligence" (EQ). The class is built as highly interactive action that starts with Q&A on a particular component of the EQ toolkit and then culminates in the intensive group and personal exercises. Considering the circumstances, we'll try to make this course as interactive as possible with your active participation in online class – sessions. Unlike your favorite hard skill classes, this course is lighter on homework, but harder on class participation. However, you will be given quite a number of home assignments, relating to self-awareness, emotional intelligence, and other leadership competences development.
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Maxim Kiselev | 3 | E&I | MC030011 | |
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Mapping Class Groups (Term 1B-2)
For an oriented surface (2-manifold) S, the group Diff(S) of orientation-preserving diffeomorphisms of S is a huge infinite-dimensional topological group. By definition, the mapping class group of S is the group Mod(S) obtained from the group Diff(S) by taking the quotient by the identity component. Equivalently, Mod(S) is the group consisting of isotopy classes of orientation-preserving diffeomorphisms of S onto itself.
Theory of the mapping class groups of surfaces lies on the crossroad of algebraic and hyperbolic geometry, three-dimensional topology, geometric, homological and combinatorial group theory. More precisely, it is related to: – Moduli spaces of complex curves (equivalently, of hyperbolic surfaces) via interpretation of the mapping class group as an orbifold fundamental group of the moduli space; – Topology of three-manifolds via interpretation of a Heegaard splitting as gluing along an element of the mapping class group; – Braids and hence knots; in fact, usual braid groups are the mapping class groups of a 2-disc with punctures. – Outer automorphism groups of free groups; this relationship is caused by the fact that the mapping class group acts by outer automorphisms of the fundamental group of the surface, and the fundamental group of the surface is not so far from being free (as it is given by 2g generators and only 1 relation). – Arithmetic groups such as SL(n,Z) and Sp(2g,Z) via the action of the mapping class group on the homology of the surface. The course will start from basic facts on surfaces and their mapping class groups. After this introductory part, we will discuss various methods in theory of mapping class groups arising from relationships listed above. |
Alexander Gaifullin |
63 per term
|
MA060459 | ||
|
Master Your Thesis in English 1 (Term 5B-6)
The Course offers concise and practical guidelines for writing and defending a Master Thesis at Skoltech. The course focuses on the main parts of the Thesis in terms of structure, vocabulary and grammar, and their transformations for a presentation with slides. Students will develop a conscious approach to own writing and presentations through thorough analyses of the best authentic examples combined with intensive writing and editing practice. The ‘process-for-product’ approach teaches the students to write – use (peer) reviewer’s advice – revise/edit – repeat and develop linguistic awareness needed to avoid the typical pitfalls in writing and live presentation.
The Course is offered in two modules which gradually build on the necessary writing and presentation skills. |
Elizaveta Tikhomirova |
31.5 per term
|
Extra | MF030003 | |
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Mathematical Methods in Engineering and Applied Science (Term 1B-2)
The course introduces students to mathematical methods widely used in modern engineering and applied sciences. It consists of three main parts: 1) Methods of Applied Linear Algebra (solving linear systems, LU, QR, SVD, and other factorizations, Principal Component Analysis, iterative methods, FFT, least squares, pseudo-inverse, etc.); 2) Statistical Methods and Data Analysis (mean, variance, probability; moments, covariance, Gaussian processes; regression, gradient descent; neural networks, machine learning); and 3) Applied Differential Equations (linear and nonlinear ordinary differential equations, stability and bifurcations of solutions, linear and nonlinear partial differential equations, hyperbolicity, characteristics, dispersion, reaction-diffusion phenomena, pattern formation).
The course is introductory by nature, covering a wide range of topics and methods of modern interest in applications. Its theoretical content is informal in style and most of the concepts will be illustrated with problems from engineering, physics, chemistry, and biology using numerical computations in Matlab and Python. The three parts of the course are aimed at: part 1 – using the right language that is crucial for understanding many computational techniques used in engineering; part 2 – learning important tools of analysis of results obtained either by computation or in experiments; and part 3 – learning the nature of key mathematical models that form the foundation of engineering and applied sciences. |
Aslan Kasimov |
63 per term
|
MA060352 | ||
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Mathematics for Engineers
The aim of this course is to refresh the basic topics that you are expected to get at bachelor level. If you do not feel confident with basic matrix manipulations, integration and differentiation, solving differential equations, operating with complex variables e t.c. – you should definitely take this course; fluency in these is a must for an educated engineer.
How to do it in 4 weeks? Sounds like impossible. Indeed, you will not recap each and every topic of your bachelor studies of math that took few years. But you'll have a chance to master some of the topics through specific engineering application. During three weeks you'll be given three sequential tasks associated with one application. Week 4 is devoted to summarizing the experience obtained and making final presentation. |
Elena Gryazina | 3 | MA030282 | ||
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Micromechanics
Micromechanics studies heterogeneous materials. They may be man-made (concrete, metals, composites, coatings) or naturally occurring (porous and cracked rocks, bone). Matrix composites – continuous matrices containing various inhomogeneities (pores, cracks, fibers, foreign particles) – constitute an important example. The goal of micromechanics is to relate the physical behavior of such materials – in particular, their overall (effective) properties – to the microstructure (geometric arrangement of the constituents and their properties). The course focuses on two groups of effective properties: the elastic and the conductive ones. The course covers the following topics:
Tensorial algebra Background results on elasticity and thermal/electric conductivity. Quantitative characterization of microstructure. Isolated inhomogeneity problem (Eshelby problem) in the context of elasticity and thermal or electrical conductivity. Property contribution tensors for effective elastic, thermal, and electric properties. Effective properties of heterogeneous materials: Variational bounds Non-interaction approximation Differential scheme Effective field approaches Cross-property connections. The lectures will be supplemented by weekly homework assignments and quizzes. Students will be evaluated on the basis of the final written exam. |
Sergey Abaimov | 3 | MA030247 | ||
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Modern Applications of Information Theory
The aim of the course is to explain modern ideas and concepts of information theory, as well as to present the emerging use-cases.
The main focus of the course are the topics on the border of information theory, communications and machine learning, in particular (a) graphical models and iterative decoding; (b) deep neural network (DNN) based data compression; (c) DNN-based channel decoding; (d) information theory based analysis of DNNs and information bottleneck; (e) Massive random access in 5G/6G and interconnection to the compressive sensing problem. |
Alexey Frolov | 3 | MA030414 | CANCELLED | |
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Molecular Biology (Term 1B-2)
Molecular biology course is based on learning the principles of replication, recombination, DNA repair. Additionally, replication strategies of phages and viruses will be discussed. Mitosis and meiosis will be described in a context of DNA biosynthesis. Also, the principles of RNA biosynthesis, i.e. transcription and processing, as well as protein biosynthesis, i.e. translation, maturation and transport will be described.
The goal of the course is obtaining a comprehensive knowledge on the structure of DNA and processes of DNA replication, recombination and repair in bacteria and eukaryotes, as well as on replication of phages and viruses. To obtain a detailed knowledge on the processes of transcription, in bacteria and eukaryotes, on the regulation of transcription in bacteria and eukaryotes, on examples of complex networks of transcriptional regulation in bacteria and eukaryotes, on maturation of RNA in eukaryotes, on protein biosynthesis in bacteria and eukaryotes, on the transport of protein in bacteria and eukaryotes. Students activities include: |
Petr Sergiev |
63 per term
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MA060034 | ||
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Neuroimaging and Machine Learning for Biomedicine
Nowadays Computational Neuroscience and Neuroimaging are fast-growing areas mostly due to new methods of acquiring, storing and processing of experimental data. Application of AI systems in clinics (for example, creating medical decision support systems) and in adjacent areas (education, pedagogics, etc.) includes processing of Neuroimaging data acquired from different devices (modern multichannel “dense” EEG systems, high-field MR-scanners, multichannel fNIRS systems, which allow precisely and non-invasively record brain activity with good spatial and temporal resolution), automation of these data analysis, and new knowledge extraction from it. The majority of relevant tasks include: classification tasks for diagnostics and prognosis, finding clinically (biophysically) significant patterns, highlighting areas of interest and others.
Neuroimaging data has several distinctive properties: it is multimodal, high-dimensional and usually very noisy. An effective analysis of these data requires understanding of the biophysical processes in the organism and the processes occurring in the scanning equipment, which are both reflected by neuroimaging data, as well as the use of a number of mathematical models that adequately describe these processes. The aims of the course are: • to explain basic ideas and results in tasks and approaches for the neuroimaging data preprocessing based on biophysical principles and processes in scanning equipment, • to give a review of various mathematical models describing the neuroimaging data reflecting their specific properties, • to teach the student how to use the conventional or modified Data Analysis and Machine Learning techniques for extracting meaningful biomarkers from the data and solving fundamental neuroscience problems as well the applied biomedical tasks. |
Maxim Sharaev | 3 | MA030421 | ||
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Neuromorphic Computing
Over the past decades the concept of neuromorphic computing that relies on imitating and exploiting the mechanisms inherent to biological nervous system has evolved into an interdisciplinary research area at the boundary between advanced computing and computational neuroscience. This direction is largely considered as one of the most promising approach to resolve the critical problems that come with continual miniaturization and ever-increasing power consumption of CMOS technology. A vast number of brain-inspired algorithms and architectures, endowing low-power requirements and massive parallel computing principles, have been attempted for applications, including complex pattern recognition, image processing, and data mining. In parallel, intensive research has been conducted for practical implementation of learning-based artificial synapses and neurons that represent two fundamental building blocks of biological neural networks.
The course is designed to provide the students with basic understanding and familiarize them with recent achievements in the field of neuromorphic engineering as implemented in artificial and spiking neural networks. In the course we will address: (i) Mathematical modeling of neurons with synapses and cognitive processes; (ii) Artificial neural networks and spiking neural networks; (iii) Temporal encoding and learning in spiking neural networks; (iv) Overview of available hardware architectures for neuromorphic computing; (v) Memristor-based neuromorphic computing. |
Dmitry Yudin | 3 | MA030407 | ||
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Parallel Computing in Mathematical Modeling and Data-Intensive Applications (Term 5B-6)
This interdisciplinary course:
— makes the students familiar with main scientific and engineering applications of modern supercomputers, — explains numerical methods behind the applications and their implementation — discusses efficiency of the common algorithms on modern supercomputer architectures — extends students background in modern processors and supercomputer architectures The applications includes computations fluid dynamics with finite difference and finite volume methods, Lattice Boltzmann and cellular automata, finite elements modeling, molecular simulations, plasma, quantum chemistry, distributed deep learning on multiple computing devices, processing big volumes of data (e.g. large graphs) on distributed systems. Each topic includes a lecture by a lead instructors, invited high-profile guest lecturers and students. Each lecture is devoted to a particular application. Students will form teams to work on projects in one of the application areas and then share their experience with the fellow students at seminar sessions and a final project presentation at the conclusion of the course. |
Sergey Rykovanov, Alexey Vishnyakov |
63 per term
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MA060411 | MOVED FROM T5-6 | |
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course
describes the intended learning outcomes and how they are assessed. The main bulk of the 81 hours of the course is spent in the actual courses in which |
Dmitry Artamonov | 3 | DG030005 | ||
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Plant Genetic Diversity and Adaptation to Stress
Plants, unlike animals, are rooted in a single location and thus forced to adapt to any environmental conditions. In order to withstand stressful conditions, plants have evolved an array of advanced physiological, biochemical, and molecular defense mechanisms, which help them in avoiding or combating negative environmental effects. Climate change is expected to exacerbate unfavorable environmental conditions caused by abiotic (drought, flooding, salinity…) and biotic (pests, diseases) stress factors, resulting in sharp reductions in average crop yields. On the other hand, societal demand for environmentally friendly farming practices with low inputs supports the need for new improved varieties tolerant to harsh environmental constraints. Understanding plant responses and adaptation mechanisms to extreme stress conditions is therefore critical to the genetic improvement of economically important crops.
Implemented in a project and problem-based learning approach, this teaching unit is built around an experimental project as a common thread throughout the course, addressing a current hot-scientific agronomic or ecological issue linked to the adaptation of plants to a key environmental constraint. |
Cecile Ben | 3 | MA030479 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Maxim Fedorov | 3 | DD030020cd | ||
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Qualifying Exam: Materials Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Alexei Buchachenko | 3 | DD030020ms | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Dimitri Pissarenko | 3 | DD030020pe | ||
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Quantum Mechanics
The course will review the basic concepts of quantum mechanics. It is intended both for those who studied quantum mechanics previously and for those who did not. The purpose of the course is not only to introduce the main principles of quantum mechanics but to familiarize with them through active problem solving, which is the only practical way to study quantum mechanics. The course will cover the main topics such as one-dimensional motion, perturbation theory, scattering theory, approximate methods in quantum mechanics, density matrix formalism.
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Konstantin Tikhonov | 6 | MA060177 | ||
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Reinforcement Learning
Reinforcement learning (RL) is a vanguard method of machine learning aimed at dynamical applications, ranging from video games to autonomous cars, robots, drones etc. Composed of an agent and an environment, it is meant to resemble the behavior of living beings somewhat. RL is truly an interdisciplinary subject that can be studied from different kinds of perspectives – machine learning, control theory, dynamical system theory, pure math (e.g. approximation theory) etc.
In this course, we dive into RL with the goal of understanding and trying out the key principles thereof. We will study how agents interact with the environment and optimize their actions to improve rewards. Speaking of examples, imagine a video game speed run. An agent, the protagonist, interacts with the game environment and wants to beat the game as fast as possible, by dynamically adjusting his or her controls while learning on-the-fly. Topics covered include policy gradient methods, actor-critic, deep RL, predictive RL and more. Convergence, safety and stability of RL are studied. |
Pavel Osinenko | 6 | MA060422 | ||
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Research Methodology: Space Center Seminar (Term 1B-4)
The seminar will cover current topics in the space domain: latest news, discoveries. Also planned that all PhD students and some Master students will present their research. External lecturers will be invited to focus on the main applications of space technologies: science, telecommunication, navigation and remote sensing. Aspects of space technologies will also be discussed: structures, software, attitude determination and control systems, on board computers, communication system power supply systems and others. The seminar will be offered in English.
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Anton Ivanov |
30.75 per term
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DG030102es | ||
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Research seminar "Cluster Integrable Systems and Supersymmetric Gauge Theories" (Term 1B-2)
This research seminar will be devoted to the study of N=2 supersymmetric gauge theories
and related topics. It turns out that comparing to the N=1 theories, N=2 supersymmetry allows to compute much more quantities. In particular, low-energy effective action can be described in terms of single function, prepotential. Seiberg-Witten solution of the N=2 theory gives explicit description of the prepotential in terms of periods of some meromorphic differential on algebraic curve. It turns out that this description is deeply related to classical integrable systems. This will be the working seminar where we are going to discuss some topics related to Seiberg-Witten theory in 4D and 5D: partition functions, relation to the integrable systems and their deautonomizations (isomonodromic deformations). On the integrable systems side we will consider cluster integrable systems (like relativistic Toda chains), which come from the dimer models or from double Bruhat cells in Poisson-Lie groups. We are going to discuss their Lax representation, discrete and continuous flows, relation to the dimer models, etc. We expect some talks given by participants. |
Pavlo Gavrylenko, Andrey Marshakov |
63 per term
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MA060461 | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1B-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and their theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, etc.
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Pavlo Gavrylenko |
61.5 per term
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DG060268 | ||
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Research seminar "Quantum Mechanics" (Term 1B-2)
Advanced course in quantum mechanics, in which the basic principles quantum theory is supplemented and applied to the study of specific physical systems. Modern methods of research of quantum systems are proposed – the construction of integrable potentials, the integral along trajectories, and the concepts of density matrix and effective action are introduced. The course involves a transition to the consideration of free field theories, their canonical quantization, and discussion of differences
quantum mechanics from quantum field theory. The purpose of the course is to consolidate the basic principles and methods of quantum theory, study the transition from quantum mechanics to quantum field theory. The course introduces the basic concepts necessary for studying the courses of the program "Mathematical physics". The course is designed as a solution to specific problems in quantum theory (see the course content). The course involves significant independent work on solving problems. I would like the results of the course to coincide with the goals. |
Vladimir Losyakov |
63 per term
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MA060464 | CANCELLED | |
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Scientific Computing
This is an introductory course to Scientific Computing with a focus on mathematical and algorithmic aspects of High-Performance Computing (HPC) techniques and their areas of applications, including both the classical model-based and modern data-driven approaches. The course has also a practical component, consisting of learning basic principles of HPC and applying the acquired knowledge and skills to solving industry-relevant problems in bioinformatics, aerospace, food and pharmaceutics, etc.
The practical aspects of the use of a variety of computational techniques for solving scientific and engineering tasks will be taught during practical demonstrations and they will be integrated as much as possible with the corresponding theoretical materials given during the lectures. All topics in the course will be covered at an advanced introductory level, with the goal that after passing the course the students will learn enough to start using scientific computing and HPC methods in their everyday research work. Students should be comfortable with undergraduate mathematics, particularly with the basics of calculus, linear algebra, and probability theory. Some preliminary knowledge of Unix-like operating systems is a plus. Although the course will overview some popular pieces of commercial software used in HPC, all of the software used for practical tasks in this course is open source and freely available. |
Dmitry Yarotsky, Nikolay Koshev |
6 | MA060113 | ||
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Soft Condensed Matter
The course is tailored to (1) CDISE Y1 Computational Science & Data science students (especially DIMMS track), as it introduces to Machine Learning application to physics (2) Petroleum Engineering students, since all topics discussed are important in hydrocarbon recovery and processing
The course introduces students to the physical, engineering and modeling aspects of soft systems, that is the systems that can be structurally altered by forces comparable to thermal fluctuations in magnitude. Soft matter is abundant in industrial processes (in particular, oil recovery and processing, detergents, adhesives, etc) and in biosystems, including the very human body. Specific topics will include interactions in emulsions and dispersions, stability of thin films and colloids, rheology, and aggregation dynamics. The emulsion block is followed by the behavior of amphiphiles, self-assembly, liquid crystals and colloids stabilization. Soft polymeric structures are next, and the final part will feature foams. The course program may be slightly altered depending on the students taking (some topics may be strengthened and some omitted if there is no interest in them). The course will be useful to all students willing to improve their understanding of natural (e.g. mineral oil) and man-made colloids and polymers. The course will also provide a hands-on experience in data-driven modelling of soft matter gained via in-class labs, homeworks and term projects. Term project will involve both physics-driven and data-driven approaches, as the students desire. |
Alexey Vishnyakov | 3 | MA030365 | CANCELLED | |
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Statistical Mechanics and Kinetics (Term 1B-2)
In this course we will consider a broad range of fundamental topics in statistical mechanics and physical kinetics. We will begin with a review of basic concepts of statistical mechanics and thermodynamics, and will then progress to more advanced themes. These include quantum degenerate gases, phase equilibrium and phase transitions (including Landau theory of second order phase transitions), theory of linear response and fluctuations, and treatment of nonequilibrium phenomena using the Boltzmann kinetic equation. Examples considered in class and homework assignments will focus on applications of the general formalism to physical systems. The course is intended for both experimentalists and theorists.
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Anton Andreev |
63 per term
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MA060339 | CANCELLED | |
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Statistical Mechanics, Percolation Theory and Conformal Invariance (Term 1B-2)
This is a course on rigorous results in statistical mechanics, random fields and percolation theory. Some of it will be dedicated to the theory of phase transitions, uniqueness or non-uniqueness of the lattice Gibbs fields. We will also study the models at the criticality, where one hopes to find (in dimension 2) the onset of conformal invariance. We will see that it is indeed the case for the percolation and the Ising model.
The topics will include: Crossing probabilities as a characteristic of sub-, super- and at- criticality. Critical percolation and its power-law behavior. The Russo-Seymour-Welsh theory of crossing probabilities – a cornerstone of critical percolation Cardy’s formula for crossing probabilities Parafermionic observables and S. Smirnov theory Conformal invariance of two-dimensional percolation a la Khristoforov. Conformal invariance of two-dimensional Ising model O(N)-symmetric models Continuous symmetry in 2D systems: The Mermin–Wagner Theorem and the absence of Goldstone bosons. The Berezinskii–Kosterlitz–Thouless transition Reflection Positivity and the chessboard estimates in statistical mechanics Infrared bounds and breaking of continuous symmetry in 3D |
Semen Shlosman |
63 per term
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MA060465 | ||
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Structural Optimization
The course is intended to give basic knowledge and skills how to formulate a structural optimization problem, including defining appropriate design variables, constraints, and objective functions. The main topics of the course are classification of structural optimization problems, calculus of variations and energy principles in solid mechanics, structural approximations, sensitivity analysis, optimality criteria, shape optimization, structures of maximum stiffness and topology optimization, SIMP-method, heuristic algorithms. Computer exercises on structural optimization will be performed with MATLAB.
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Alexander Safonov | 6 | MA060452 | ||
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Survey of Materials
The course teaches fundamentals of modern Materials Science (Part I of the course) and provides a Survey of Materials (Part II), covering all relevant Skoltech research areas and beyond, with brief explanation of structural, electronic, physical, chemical or other properties of materials relevant for their practical use, or from the point of view of utilizing their unique properties in applications.
It is a core course in Materials Science educational track providing a reference knowledge base for the rest of material-specific courses as well for student research. |
Andriy Zhugayevych | 6 | MA060063 | ||
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Symmetric Functions (Term 1B-2)
The theory of symmetric functions has numerous applications in various domains of mathematics and mathematical physics. At the beginning of the course, standard material will be presented, and then we will move on to more advanced topics.
Tentative program: The algebra Sym of symmetric functions. Generators of Sym. The scalar product, involution map, and Hopf algebra structure. Schur functions, skew Schur functions. Combinatorial formula. Cauchy identity and dual Cauchy identity. Jacobi-Trudi formula and its dual version. |
Grigori Olshanski |
63 per term
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MA060458 | ||
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Technology Entrepreneurship: Seminar (Term 1B-2)
The course is designed to help you to master practical skills of technology entrepreneurship and to accelerate your startup projects up to “external support/funding ready” level. It is intended for students: (1) interested in new tech venture creation and technology entrepreneurship; (2) having their own projects/ideas in development; (3) planning startup contest participation, pitch to investors, applying to Skolkovo Foundation, “Bortnik Fund”, STRIP, accelerator/incubator program, etc. The startup project concept may be in the experimental mode, or further along in its evolution such as seeking customers or pilot tests.
The course will be conducted as practical and hands-on lab. We will not study entrepreneurship as a theoretical subject through external cases or papers. The material for learning comes directly from the class projects and the issues faced by students in converting their projects into successful ventures. This assures projects accelerated development and creates a highly dynamic environment for teaching where the faculty is a facilitator, mentor, tracker, and lecturer at the same time. Within the course project teams are expected to develop and deliver various presentations on selected aspects of their business idea. Each class member is expected to contribute actively to the discussions and presentation critiques. The course subject areas represent “golden standard” for tech startups: (1) Problem and Market Need; (2) Product and Technology; (3) Market evaluation; (4) Business Model; (5) Startup Team; (6) R&D/Marketing/Sales/Funding plan; (7) Storytelling and Pitching. Upon the course completion, you and your team will |
Alexey Nikolaev |
31.5 per term
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E&I | MC030029 | |
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory 6 credits component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 | ||
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Unconventional Hydrocarbons
The course provides an introduction to unconventional (shale) hydrocarbons as a perspective source of oil and gas. It consists of several parts describing existing oil and gas shale formations in a world and in Russia (Bazhenov, Domanik, Khadum formations), detailed data on lithology, petrophysics, geochemistry and geomechanics of shale rock and modern methods for prospecting, exploration and production of unconventional hydrocarbons. The course includes lectures, seminars and laboratory works. During the course students work individually and in teams compiling a comprehensive data set, analyzing of research results and developing of technological strategy on prospecting, exploration and production of shale hydrocarbons.
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Mikhail Spasennykh, Alexei Tchistiakov |
6 | DA060189 | CANCELLED |
Term 2
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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Academic Communication: Preparatory English for Phd Exam (Term 1-2)
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. The course is aligned with the NATURE MASTERCLASS available to Skoltech researchers, so you will be able to benefit from professional recommendations of the Nature experts regarding the structure and contents of a publication, and constructive feedback from your Instructor on the language of your materials. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities, to be performed in class and at home, individually and in teams. By the end of the course, successful participants will – know the rules and conventions of research paper writing, including structure, style, grammar and vocabulary; – improve their academic communication skills, such as active listening, spontaneous and rehearsed speaking/ presentation, reading and writing within a given academic genre; – have experience in writing, editing, peer-reviewing and presenting research results. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | DF030029 | |
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Aerosol Science and Technology
The course will introduce the basic phenomena of aerosol science, particle formation in the gas phase and their behavior, concepts and measurement techniques for the aerosol particles. Students will synthesize (carbon nanotubes, NaCl, metal, metal oxide and polymer) nanoparticles by two aerosol techniques: gas-to-particle and liquid-to-particle conversions. Students will be trained to operate spark-discharge aerosol synthesis reactor for production of nanoparticles and single-walled carbon nanotubes and spray drying and pyrolysis reactors.
The student will perform the on-line measurements of number size distribution of aerosol synthesized nanoparticles by differential mobility analyzer (size range: 2-1000 nm). Students will become familiar with processes of the aerosol particle collection (filtration, electrostatic precipitation, thermophoretic precipitation). The produced samples of nanoparticles will be observed with means of transmission and scanning electron microscopies. Totally 34 lecture hours and 15 exercise hours, 5 hours for seminar lessons, 6 presentation hours will be arranged. Students will write a short essay and give a presentation on one of the selected topics. |
Albert Nasibulin | 6 | MA060300 | ||
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Affine Quantum Groups (Term 1B-2)
The main source of the theory of quantum groups was integrable models. Affine quantum groups (better to say quantum universal enveloping of affine Lie algebras) are symmetries of the XXZ model, they appeared simultaneously with quantum group theory in the mid-80s. Affine quantum groups have many new properties (compared to quantum universal enveloping algebras of simple Lie algebras) — different realizations, different comultiplications. They have many applications, in addition to the integrable models mentioned above, we mention cluster algebras and geometric representation theory.
In the course, we will discuss the basic constructions associated with quantum affine algebras and touch on their applications. Some basic familiarity with quantum groups and affine Lie algebras is assumed, and familiarity with the Bethe ansatz is also desirable. |
Mikhail Bershtein |
63 per term
|
MA060462 | ||
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Bayesian Methods of Machine Learning (Term 1B-2)
The course addresses Bayesian approach for solving various machine learning and data analysis problems. It offers the framework for solving inverse problems, model selection and continual learning. The most attention is payed to the fusion between the deep learning techniques and the elements of bayesian approach. This fusion is possible due to the development of a range of approximate inference techniques in the last 20 years.
Modern machine learning papers frequently use machinery of the approximate inference techniques. The main learning outcome of the course is the ability to read and reproduce related papers, and to apply corresponding methods of approximate inference for the development of Bayesian machine learning approaches. In order to reach this goal, the course contains theoretical and practical assignments and the final project. The purpose of the theoretical tasks is two-fold. Firstly, we would like to develop skills of equation derivation, that is used routinely in papers and is usually suppressed. Secondly, some theoretical tasks provide the intuition about properties of the methods through toy models. The purpose of the practical tasks is straightforward: to translate discussed methods to the code and observe properties of the methods through examples. These assignments leads to the course projects: reproducing and discussing a relevant paper from a recent conference, and/or development of some students' research ideas using methods of Bayesian machine learning. |
Evgeny Burnaev |
63 per term
|
MA060129 | MOVED FROM T5 | |
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Bioinformatics
This course is aimed for the first year master students with no prior knowledge in bioinformatics. The main course idea is to get students from different backgrounds acquainted with basic tools and algorithms in bioinformatics, such as basic phylogenetic analysis, algorithms behind sequence alignments, protein structure analysis, gene and genome annotation, as well as main databases storing sequence, protein and epigenetics data.
The knowledge obtained during this course can be immediately utilized in the real biological and computational projects. |
Mikhail Gelfand | 6 | MA060307 | ||
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Biomedical Innovation and Entrepreneurship
The course aims to provide students with an understanding of applications and practices of biomedical science in an industrial healthcare. To put it simple, we will discuss where and how Skoltech biomedical graduates may employ their skills beyond academy science. To achieve this goal the course will decompose the industry into the value chain of independent but interconnected entities and then make deep investigation of motives, profits, and costs of any segment/entity of this value chain. The incomplete list of such entities will include: R&D-driven startups, CROs, CMOs, regulators, integrated pharmas, marketing agents, distributors, retail, hospitals, doctors. The emphasis will be made on the value chain groups that are immersed into the challendge of transforming high technologies into the tangible patient benefit, from hardcore drug development to all kinds of medical devices and services. Such challenges will be taught through development of the group project that will be developed through the stages of Problem statement (indication, regulation, POC and QC), preclinical design, clinical design, manufacturing/delivery design and final integrative presentation.
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Dmitry Kulish | 3 | E&I | MC030013 | |
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Biomedical Mass Spectrometry (Term 5B-6)
This course introduces students to the first principles and methods of mass spectrometry with special emphasize on biological and medical applications. The course will cover wide range of mass spectrometry techniques used for ion generation, separation, detection and data processing and interpretation. The course will teach the theoretical fundamentals required for the design of instruments and methods for measuring mass spectra of biological samples. The course will cover mass spectrometry applications in OMICs technologies, mass spectrometry applications in biomarker discovery and tissue imaging.
After successful completion of this class, students will acquire the initial knowledge of the operational principles and design of different mass spectrometers, different methods of ionization of biological molecules of wide mass range, different methods of ion separation including magnetic sector, time of flight, RF and DC ion traps, as well as FTICR. Experimental and bioinformatics based methods of protein, peptides, lipids and metabolite molecule identification, different fragmentation methods for primary and secondary structure determination, methods of quantitative determination of proteins, lipids, metabolites and small molecule in physiological liquids |
Evgeny Nikolaev |
63 per term
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MA060256 | MOVED FROM T5-6 | |
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Cancer Biology
The course is dedicated to basics of clinical and molecular cancer biology with emphasis on innovative drugs and technologies for cancer treatment and diagnostics.
The main themes to be discussed: What is cancer? Classification, staging, grading, еhe hallmarks of cancer; Mutations, oncogenes, tumor suppressors, пenomic instability, epigenetics in cancer initiation and progression (miRNA, siRNA, lncRNA, methylation, histones modification, etc.); Cell cycle and growth and it’s deregulation in cancer; Tumor energy metabolism (the Warburg effect); Cell death and it’s deregulation in cancer (necrosis, apoptosis, autophagy); Cell differentiation and dedifferentiation in cancer, EMT, Metastasis; Cancer stem cells hypothesis; Tumor stroma and heterogeneity, neoangiogenesis; The “seed and soil hypothesis” – pre-metastatic niche; Extracellular vesicles for communication between cells; Cancer diagnostics, tumor markers; Liquid biopsy; Biological factors in Cancer (inflammation, viruses, bacteria, microbiome); Target therapy, Immune checkpoint inhibitors; Emerging therapeutic modalities, Car-T, dendritic cells, viruses; Invited medical oncologists will give talks on the usolved questions in the field. |
Vera Rybko | 3 | MA030088 | ||
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Classical Integrable Systems (Term 5-6)
Course description: A self-contained introduction to the theory of soliton equations with an emphasis on their algebraic-geometrical integration theory. Topics include:
1. General features of the soliton systems. 2. Algebraic-geometrical integration theory. 3. Hamiltonian theory of soliton equations. 4. Perturbation theory of soliton equations and its applications to Topological Quantum |
Igor Krichever |
63 per term
|
DA060179 | ||
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Computational Chemistry and Materials Modeling
Please see the course website for syllabus and other information: http://zhugayevych.me/edu/CC/index.htm
The course provides a graduate level overview of modern atomistic computer simulations used to model, understand and predict properties of technologically important materials. The emphasis is on practical use of techniques, algorithms and programs to bridge theory and applications, from the discovery of materials to their use in real-world technologies. Several laboratories give students direct experience with simulation methods as well as practical knowledge on how to use computational modeling and how to present and interpret results of simulations. Bridges from atomic to complex systems demonstrate potential of different theories to applications relevant to multiple major industries in the future, including nanotechnology and energy. |
Andriy Zhugayevych | 6 | MA060008 | ||
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Computational Materials Science Seminar
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov | 1.5 | MA030430i | CANCELLED | |
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Computational Methods in Genetics
Given the wealth of raw genomics data, the challenge is now to get the most of observations.
Large amounts of genomic data allow unprecedented opportunities to map the genotypes and the traits. This raises the question of the most suitable computational methods to implement. The course will first present the current methods used to estimate the recombination fraction and build genetic maps in plants and animals. Practicals and workshops will allow the attendees to succeed in computing linkage maps, in diverse situations in plants or animals. The second corner-stone of analytical tools is Linear Models. |
Laurent Gentzbittel | 3 | MA030482 | MOVED FROM T3 | |
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Continuum Mechanics
Continuum mechanics is a section of mechanics and theoretical physics, or rather the continuation of theoretical mechanics that deals with analysis of deformable bodies. However, mathematics in continuum mechanics represents the main constructive tool. Continuum mechanics allows to demonstrate the power of logic and mathematical thinking. Based on a few fundamental postulates and principles, using the mathematical apparatus can reveal non-trivial, and even striking results.
Foundation of continuum mechanics consists of: This course uses tensor representations in the Cartesian coordinate system of the observer. But it will one shown in detail how to to write the continuum mechanics equations in the arbitrary curvilinear coordinate system. This way the common link is not lost and the exposition becomes easier and clearer. |
Robert Nigmatulin | 6 | DA060181 | ||
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Convex Optimization and Applications
This course introduces the basic theory in convex optimization and illustrates its use in recent successful applications such as sparse learning, blind source separation, low-rank optimization, image processing, regression and classification, phase retrieval.
Through the course, students will study convex sets and functions and their properties, duality and dual maximization problem with the same optimal value, a certificate of optimality for an optimization problem. Students will also learn some commonly known convex optimization forms such as Linear Program, Quadratic Program, Second Order Cone Programs, Semi-Definite Programs etc. Students will know practical tools, and able to recognize and formulate convex optimization problems and solve them using efficient solvers. |
Anh-Huy Phan, Andrzej Cichocki |
3 | MA030136 | ||
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Crystal Chemistry
This course will be useful to a diverse group of students involved in designing new functional materials including but not limiting to electrode materials and solid electrolytes for rechargeable batteries. This course has two objectives: (i) to familiarize with the key parameters and laws that govern the organization of crystalline matter and related crystal structures, (ii) to give a comrehensive guide of to how to "read", understand and "tune" crystal structures to design, create and modify functional properties of materials.
Students will learn what is required to understand the potential of a new structure to become a base for a prospective material. The first part of the course will cover general structure types, their organization and pecularities, reveal essential crystal chemistry concepts, rules and laws. With these knowledge at hand, the students are invited to the second part of the course to be acquainted with the crystal structure organization of modern electrode materials for rechargeable batteries. The acquired knowledge and skills are expected to facilitate the creation of completely new materials with outstanding characteristics that find applications in technological problems related to energy storage and conversion. The content of the course is not exclusively fundamental but relates to practical aspects of the creation of innovative materials via a crystal chemistry design approach. |
Stanislav Fedotov | 3 | MA030477 | ||
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Differential Geometry of Connections (Term 1B-2)
In this course we present the basic concepts of modern differential geometry: metric, curvature, connection, etc. The goal of our study is to develop tools for practical efficient computations (including the art of manipulation with indices) supported by a deeper understanding of the geometric meaning of all notions and theorems. We will develop an approach based on the notion of connection with all its different aspects: covariant derivative, parallel transport, collection of Christoffel symbols, matrix-valued one-form etc.
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Maxim Kazarian |
63 per term
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MA060460 | ||
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Digital Signal Processing
Digital signal processing (DSP) refers to various techniques for improving the accuracy and reliability of digital communications. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, statistical signal processing, estimation theory, signal sampling, re-sampling, and reconstruction free of errors. We will analyze signal distortions due to sampling, interpolation, and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. We will also deal with modulation and multipath propagation channel models. Hands-on examples and demonstrations will be routinely used to close the gap between theory and practice.
The extra topics covered in this course are: – Spectrum analysis; – Machine learning for signal processing; – Fundamentals of random signal theory and analysis; – Modeling communication signals as random processes; – Baseband signal processing, signal synthesis, and filter design for communication; – Statistical signal processing in communication; It is hoped that through learning this course students will be equipped with a clear picture of DSP as well as a necessary foundation for further study of advanced DSP topics in the future. |
Andrey Ivanov | 6 | MA060255 | ||
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Efficient Algorithms and Data Structures
Design and analysis of algorithms and data structures is a core part of Computer Science and is of fundamental importance to all application areas. The goal of this course is to provide a representative sample of advanced algorithmic notions and techniques that constitute a modern toolbox for solving real-life problems. We will mainly deal with basic discrete objects – sets, trees, graphs, strings, … – and present efficient data structures and algorithms for solving various basic problems on these objects. Therefore, this course can be viewed as a basis for more specialized subjects. Lecture part of the course will focus on principles and ideas as well as on their mathematical justification. The practical part will include programming exercises including homework assignments that will be graded through an automatic code-testing system codeforces.com. These will strengthen practical problem solving skills using techniques taught in the course.
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Gregory Kucherov | 3 | MA030270 | ||
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Emerging Technologies for Next Generation Wireless Communications
Fifth generation mobile communications, rapidly deploying all around the globe, are promised to outperform the available solutions in terms of latency, energy-efficiency, and data rates. However, with its peak speed of about 10 Gb/s and channel bandwidth of 0.1-1 GHz this technology will turn out to be inadequate to meet the explosive growth of machine connectivity in the short run. This course is designed to provide the students with basic knowledge of terahertz technology, edge AI hardware design, and reconfigurable intelligent surfaces which are customarily identified as enabling technology towards wireless mobile communications beyond 5G. We will specifically touch upon reconfigurable intelligent surfaces that can potentially lead to enhanced energy and spectrum efficiency of wireless communications. In particular, we will elaborate on space-time-coding digital metasurfaces that are constituted by a programmable array of artificial unit cells each of which is characterized by its own electromagnetic response.
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Dmitry Yudin | 3 | MA030473 | CANCELLED | |
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Energy Systems Physics and Engineering
Classical equilibrium thermodynamics is a theory of principles, which provides a framework to study means to produce motive power and useful heat, crucial for our everyday life. It is a pillar of any serious physics and engineering curriculum. This graduate course provides the students from possibly diverse backgrounds with the theoretical concepts that underlie the physics of energy conversion at the heart of heat engines operation, including chemical processes, and the specific knowledge of energy technologies in use nowadays.
Covering some of the main real-world technologies for the generation of electric/mechanic, heating and cooling power: boilers, steam and organic Rankine cycles, gas turbines, internal combustion engines, heat pumps and chillers to name a few, students will learn to critically analyze and assess these technologies to improve their performance and imagine innovative and commercially viable solutions to energy problems, accounting for costs and environmental aspects like pollutants formation and their abatement. Essential notions which are taught include: energy conversion; heat transfer; work; first and second principles; working fluids and thermoelastic coefficients; thermodynamic cycles; motors and refrigerators: engines and heat pumps. Time permitting, basic notions of finite-time thermodynamics may be briefly introduced. The course is organized around the learning of essential concepts and an awareness development of current energy technologies. It is based both on "teaching with lecture" and "teaching with discussions" methods. In addition to home assignments and project, students will solve problems during tutorials and discuss their solutions. |
Henni Ouerdane | 6 | MA060001 | ||
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English
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research and follows the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, last of three weeks and take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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English Toolkit
The goal of the English Toolkit course is to activate Academic English skills required for successful education at Skoltech.
The students will practice Academic vocabulary and grammar, as well as boost their reading, writing, listening and speaking skills within a range of research-related topics. The chosen format provides the students with a flexible and individualized learning trajectory. Real-time feedback for online exercises is complimented by tutor feedback for the writing and speaking assignments for a better understanding of the main language difficulties, providing an opportunity to improve and see progress. By the end of the course, the students will |
Elizaveta Tikhomirova | 3 | Extra | MF030001 | |
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Entrepreneurial Finance
This course trains future scientists and technology entrepreneurs to apply theories and methods of entrepreneurial finance, as well as the to develop financial models for the creation of new goods and services.
Course participants are also prepared to build criteria for evaluating financial alternatives, gathering data and analyzing financial and market information, financial analysis for making strategic decisions, and the necessary entrepreneurial skills needed to manage a financially viable business |
Alexander Chekanov | 3 | E&I | MC030028 | CANCELLED |
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Entrepreneurial Strategy
This course focuses on how scientists and technology entrepreneurs identify, design and implement strategies to sustain and enhance the success of the commercialization of their discoveries, examining issues central to the long- and short-term competitive position of the scientific and technological discoveries and developments they are willing to commercialize and transfer to society.
As a field, Strategy attempts to explain why and how some organizations outperform others in the marketplace, developing competitive advantages around technology, science or, overall know-how, and sustaining these advantages over time while remaining competitive. The course provides a set of frameworks and analytical tools that enable scientists and technology entrepreneurs to understand and plan effective strategies for competing with their technologies in a range of industries. |
Alexander Chekanov | 3 | E&I | MC030023 | |
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Evolutionary, Population and Medical Genomics
Nothing in biology makes sense except in the light of evolution. This course introduces the fundamentals of evolutionary science as applied to genomics. It will allow to see how the basic population genetics processes create, maintain and affect variability in populations and lead to their changes with time. The focus will be on molecular evolution, i.e., the manifestation of these processes in genomes. As humans, we will be particularly interested in evolutionary aspects of medicine. The course assumes no prior familiarity with evolutionary biology, although knowledge of the basics of molecular biology and genetics is expected. The themes covered will include basic concepts in evolutionary biology and generalizations in evolutionary genomics; population genetics and factors of microevolution; and basics of quantitative genetics.
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Georgii Bazykin | 6 | MA060222 | ||
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Facilitating and Assessing Learning (Term 1B-2)
The course offers an introduction to facilitating learning in higher education for junior faculty together with PhD student TAs. The course content focuses on aligning learning outcomes with learning activities and assessment strategies. Constructive alignment in the course is defined at high resolution such that learning outcomes for a course are elaborated into separate activities and assignments for students. In other words, learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. The assessment design that participants in the course design will therefore be required to reflect significant and effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments, and the course therefore comes with an emphasis on communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course contains discussion topics to highlight ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching and course development or their supervisory activities. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson |
31.5 per term
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DG030030 | ||
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Fundamentals of Photonics
This course is aimed for the first year master students, and provides an overview of the main physical principles of photonics and photonic devices. The emphasis is made on the demonstration that the light matter interaction in photonic devices can be modified by means of the modern technology. The course give the illustration how the basic physical laws help to get qualitative understanding of different branches of photonics such as light emission, transmission and detection. This introductory course is designed for both theoreticians and experimentalists.
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Nikolay Gippius, Ildar Gabitov |
6 | MA060160 | ||
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Fundamentals of Power Systems
This course covers power systems analysis & operations, including fundamentals (balanced three-phase power), steady-state analysis (power flow), state estimation, operation (optimal power flow), security (contingency analysis and security-constrained optimal power flow), distribution grid operation, and challenges and trend of future power systems. After successfully completing this course, the student will be capable of analyzing the technical and economic operation of an electric energy system.
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David Pozo | 6 | MA060007 | ||
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Geometric Representation Theory (Term 1B-2)
Geometric representation theory applies algebraic geometry to the problems of
representation theory. Some of the most famous problems of representation theory were solved on this way during the last 40 years. The list includes the Langlands reciprocity for the general linear groups over the functional fields, the Langlands-Shelstad fundamental Lemma, the proof of the Kazhdan-Lusztig conjectures; the computation of the characters of the finite groups of Lie type. We will study representations of the affine Hecke algebras using the geometry of affine Grassmannians (Satake isomorphism) and Steinberg varieties of triples (Deligne-Langlands conjecture). This is a course for master students knowing the basics of algebraic geometry, sheaf theory, homology and K-theory. |
Mikhail Finkelberg |
63 per term
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DA060271 | ||
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Heterogeneous Volume Modeling and Digital Fabrication
The course covers methods and techniques of digital modeling of volumetric point sets with attributes presenting pointwise properties such as material fractions, color, and other volumetric object properties. Modelled objects are characterized by complex volumetric geometry, multi-scale microstructures and volumetric multi-material density distribution. Stress will be made on using continuous and discrete scalar fields for modelling both geometry and attributes. Associated methods of multi-material digital fabrication will be outlined.
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Alexander Pasko | 3 | MA030299 | CANCELLED | |
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High Performance Python Lab
This course is devoted to learning how to use Python for High Performance Computing on different architectures – multi-core CPUs and general purpose GPUs.
The course is oriented on practical knowledge, where the students will get a hands-on experience with Python code profiling, modern Python frameworks, such as Python MultiProcessing, Numba, Cython, mpi4py, PyCuda and others. Wide range of problem sets from linear algebra, image processing, deep learning, physics and engineering makes this course interesting and suitable for all levels of students from all CREIs. Students will also get the possibility to work on modern supercomputers. |
Sergey Rykovanov, Daniil Stefonishin |
3 | MA030367 | ||
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History and Philosophy of Science
The aim of this course is to give to Skoltech students and postgraduates basic information about the main stages of the development of science from its birth in Ancient Greece through the Middle Ages and the Renaissance to Modern Times and to the great scientific revolutions of the XX century. Every man of culture especially a future scientist should know the impact of such great thinkers as Plato, Aristotle, Thomas Aquinas, John Buridan, Nicholas of Cusa, Copernicus, Galileo, Descartes, Newton, Boscovich, Darwin, Mendel, Bohr and Einstein (omitting many other brilliant names, that would be spoken about in the frames of the course) to the development of a scientific picture of the universe. Also there will be discussed the main topics and notions of the philosophy of science: demarcation between science and humanities, Popper’s theory of falsification, Kuhn’s theory of scientific revolutions, philosophical ideas of Lakatos and Feyerabend.
The course will consist of 18 3-hour lectures and 6 examination sessions (3 hour each). The students are to submit 6 written essays in English on the following themes: 1. Ancient Greek and Roman Science; 2. Medieval and Renaissance Science; 3. Scientific Revolution of the XVII century; 4. Science in the XVIII and XIX century; 5. Science in the XX-XXI centuries; 6. Philosophy of Science. For the final exam the students should prepare 10-15 minutes oral presentation on the scientific problem they are currently working upon, or on some topic connected with history of science and/or philosophy. Lectures 1-3 are devoted to the Ancient Greek and Roman Science Lectures 4-6 are devoted to Medieval and Renaissance Science Lectures 7-9 are devoted to the Scientific Revolution of the XVII century Lectures 10-12 are devoted to Science in the XVIII and XIX century Lectures 13-15 are devoted to Science in the XX-XXI centuries Lectures 16-18 are devoted to the Philosophy of Science |
Ivan Lupandin | 6 | DG060026 | ||
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Ideas to Impact: Foundations for Commercializing Technological Advances
Technological innovation is critical to the survival and competitiveness of emerging and existing organizations. This course lays the foundation to undertake a robust analysis and design of opportunities for technology-based commercialization. We introduce tools and frameworks that help isolate and control the factors shaping the identification, evaluation and development of commercial opportunities. Throughout the course we use technology examples originating from problem sets found in engineering and scientific education to develop the skills necessary to connect technology and impact.
At the same time, through creativity lab students will be introduced to a variety of creative problem solving techniques and learn how to apply these techniques in the context of the development, evaluation and application of ideas and concepts with commercial potential; consider the evaluation of business ideas that translate existing business models into new national contexts. The course is designed to help students develop the ability to find, evaluate, and develop technological ideas into commercially viable product and process concepts, and build those concepts into viable business propositions. The material covered is research and theory-based but the course is practice-oriented with much of the term spent on shaping technology-based opportunities. A central objective of this subject is to equip students with an understanding of the main issues involved in the commercialization of technological advances at both strategic and operational levels. |
Zeljko Tekic | 6 | E&I | MC060002 | CANCELLED |
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Industrial Robotics
Industrial robots offer substantial gains in manufacturing productivity, particularly when integrated into an automated system. A great range of capabilities commonly includes a wide variety of basic industrial tasks: material handling and palletizing, machine loading, parts assembly, welding, spray painting, and tool operation.
This practical-oriented course aims to familiarize graduate students with intelligent industrial robots and gain valuable hands-on skills through extensive training sessions and utilizing modern concepts and techniques of controlling robot manipulators and types of end effectors. Also, students will be trained to handle different automation tasks required by the needs of a particular industrial sector. |
Dmitriy Dzhurinskiy | 3 | MA030249 | ||
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Introduction to Blockchain
This course provide an overview of modern blockchain technology and its' practical applications (Cryptocurrency, Certification, Anchoring. Industrial examples.) We will start from basic cryptography and distributed data base systems and show how these tools are used in blockchain. The covered topics are the following:
-) Introduction to cryptography, type of ciphers. Private and Public crypto systems |
Alexey Frolov, Yury Yanovich |
3 | MA030272 | ||
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Introduction to Computational Fluid Dynamics
Fluid flows are ubiquitous in engineering. Fluid mechanics provides the theoretical foundation to a broad spectrum of engineering applications that range from tiny laboratory-on-a-chip devices to the largest thermal and hydroelectric power plants. The rich dynamics of fluid motion leads to numerous effects that engineers may wish to exploit or suppress. Mathematical description of fluid flows most commonly involves non-linear partial differential equations that make analytical solution impossible or impractical. Therefore, approximate solution using numerical methods has been widely implemented since the advent of digital computers. Nowadays, the Computational Fluid Dynamics (CFD) is a well-established discipline. CFD is widely used in science and engineering alike. It accelerates optimal design and offers important insights in the flow dynamics.
The course will introduce the students to important theoretical and practical aspects of the CFD. It will explain how to describe the fluid flow by partial differential equations with suitable initial and boundary conditions, and how to transform those equations into computer algorithms. A brief overview of general-purpose numerical methods will be provided, with comments on their relevance to the CFD. Then, specialized methods for different types of flows will be introduced. Computer practice classes will allow the students to acquire basic skills of programming simple methods from scratch, get acquainted with existing CFD software packages, develop intuition for distinguishing physical effects from numerical artifacts, and learn to use the CFD wisely. |
Dmitry Kolomenskiy | 3 | MA030455 | MOVED FROM T5 | |
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Introduction to Computer Vision
Computer Vision is one of the most rapidly evolving subfields of Data Science with many applications, e. g. in autonomous driving and healthcare, among others. This course is designed to provide a comprehensive systematic introduction to the field. We'll start with the recognition of some simple object elements such as corners and edges and then proceed to the detection of more complex local features. All major problem statements such as image classification, object detection and segmentation as well as the corresponding classical algorithms will be covered within the course. Finally, we'll briefly introduce convolutional networks and discuss key deep learning architectures for the same set of problems.
We'll extensively use Python and CV & image analysis libraries scikit-image and OpenCV during hands-ons and homeworks. The final grade will be calculated using the results of three homeworks (20% each) and the final project (40%). |
Mikhail Belyaev | 3 | MA030348 | ||
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Introduction to IoT
In the last decade the Internet of Things (IoT) paradigm has slowly but steadily and increasingly permeated what researchers and engineers study and build. The term “Internet of Things” doesn’t have a single definition and people today often use it to interchangeably refer to Wireless Sensor Network (WSN), Machine-to-Machine (M2M), Web of Things (WoT) and other concepts. The focus of this course is to learn about these technologies that will be extending the Internet as we know it and use it today, to interconnect not only people and computers but also sensors and associated objects. The course will be divided into two strongly coupled parts. The first part of the course covers the IoT ‘pillar’ technologies, i.e. embedded systems, wireless sensor networks, semantic technologies, and theory behind them while the second part will have a special focus on IoT development, i.e. IoT apps, open platforms, sensors and actuators, software/middleware. Apart from covering the theory behind the IoT and “how to connect things to the Internet”, the course will therefore also engage the students to demonstrate the feasibility of simple IoT real applications and will challenge them to improve their applications through the use of cognitive technologies and cloud computing.
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Andrey Somov | 3 | MA030233 | ||
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Introduction to Linux and Supercomputers
The course is devoted to series of frequently asked questions from people who start their scientific computing life with Linux. We'll give a masterclass for a work within the ssh-session, standard terminal commands and their combinations, tips on organization of the simplest possible bash-scripts (loops, background calculations, IO-redirections, etc.).
We'll explain and demonstrate the gentleman's set for software compilation from source (user configuration files, environment variables, Makefiles basics, compiler options and optimization flags, linking external libraries and connection of these concepts). We'll describe then the very basic points of hard- and software architectures of modern computing systems. And in the end of the day we'll present the model project based on all the concepts above. Lecturers expect that after the course student will be able to login on the HPC-cluster, properly setup the environment, compile the source code, run parallel programs on HPC-systems and write scripts for data post-processing. |
Igor Zacharov | 3 | MA030366 | ||
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Introduction to Programming for Biologists
Science is constantly changing. The more and more complicated analyses are needed for publication, not only for a theoretical study but also for experimental research. In the light of ongoing development of new high-throughput experimental techniques, the skill of data processing and analysis becomes inevitable for any researcher.
One of the most used tools to process data and make publication-quality plots is Python programming language. Python is simple, powerful, and extremely popular. Among programming languages, Python is the closest to natural languages making it easy to begin with. In the course “Introduction to Programming for Biologists” I will provide basic knowledge of Python in a simple form suitable for understanding by all students, especially by completely unfamiliar with programming. With many exercises, students will inevitably master their programming skills to the level appropriate for processing their own data and making basic-to-medium analyses. |
Dmitry Ivankov | 3 | MA030372 | ||
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Introduction to Solid State Physics
This course gives an introduction to solid state physics, one of the cornerstones on which a modern technologies are based. We will begin with the conventional classification of solids on the basis of binding forces, to be followed by crystal structure description and experimental techniques for crystal studies. From there we will discuss classical and quantum aspects of lattice dynamics and begin the study of the electronic structure of solids, including energy band formalism and carrier statistics in metals, semiconductors and insulators. Next we will consider basics of the atomic and electronic processes at crystal surfaces and interfaces, kinetic effects and scattering of the electrons. We will continue with the study of the optical processes in solids, including nonequilibrium carrier dynamics and photovoltaic effects and their applications in technology.
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Sergey Kosolobov | 6 | MA060027 | ||
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Introduction to Surface Physics
This course assumes the study of techniques able to provide information concerning electronic and atomic surface structure. The techniques can be applied to materials and nanostructures research. The students should learn how the surface is arranged, what are the specific properties of the surface, what processes occur at the surface and interfaces, including metal-semiconductor interface and some other interfaces typical for heterostructures. Adsorption, interfacial reactions and films growth are also considered. Ultra-High Vacuum techniques of surface characterization are accented.
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Andrey Ionov | 3 | MA030218 | ||
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Introduction to Thermal Spray Coatings
Thermal spray technology provides a cost-effective, functional surface solution for many applications requiring materials resistance to wear, heat, and corrosion. This introductory and practically-oriented class is intended to familiarize graduate students with an understanding of thermal spray processing science and front-line research topics, focusing on the latest development and innovations in the field.
Students’ key learning objectives: Develop knowledge and specific hands-on skills in thermal spray processing of materials; Perform spraying of single- and multi-phase compositions, ceramics, metal-matrix, and functionally gradient materials; Carry out physical properties evaluations of sprayed deposits and assessing coating microstructural features using characterization methods and analysis tools. |
Dmitriy Dzhurinskiy | 3 | MA030453 | ||
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Magnetic Phenomena at Macro-, Micro- and Nanoscales
Objectives of this course are as follows: the mastery of the fundamental concepts, laws, experimental results and theories of the rapidly developing field of spintronics. Spintronics involves study of active control and manipulation of spin degrees of freedom in solid-state systems. The primary focus of the course is on basic physical principles underlying
the generation of carrier spin polarization, spin-polarized transport in metals, semiconductors and insulators, spin dynamics and control of magnetization. The basic principles are illustrated by direct calculations in the framework of simple and transparent physical models. A number of problems are suggested for individual work followed by subsequent group discussions. |
Irina Bobkova | 3 | MA030219 | ||
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Mapping Class Groups (Term 1B-2)
For an oriented surface (2-manifold) S, the group Diff(S) of orientation-preserving diffeomorphisms of S is a huge infinite-dimensional topological group. By definition, the mapping class group of S is the group Mod(S) obtained from the group Diff(S) by taking the quotient by the identity component. Equivalently, Mod(S) is the group consisting of isotopy classes of orientation-preserving diffeomorphisms of S onto itself.
Theory of the mapping class groups of surfaces lies on the crossroad of algebraic and hyperbolic geometry, three-dimensional topology, geometric, homological and combinatorial group theory. More precisely, it is related to: – Moduli spaces of complex curves (equivalently, of hyperbolic surfaces) via interpretation of the mapping class group as an orbifold fundamental group of the moduli space; – Topology of three-manifolds via interpretation of a Heegaard splitting as gluing along an element of the mapping class group; – Braids and hence knots; in fact, usual braid groups are the mapping class groups of a 2-disc with punctures. – Outer automorphism groups of free groups; this relationship is caused by the fact that the mapping class group acts by outer automorphisms of the fundamental group of the surface, and the fundamental group of the surface is not so far from being free (as it is given by 2g generators and only 1 relation). – Arithmetic groups such as SL(n,Z) and Sp(2g,Z) via the action of the mapping class group on the homology of the surface. The course will start from basic facts on surfaces and their mapping class groups. After this introductory part, we will discuss various methods in theory of mapping class groups arising from relationships listed above. |
Alexander Gaifullin |
63 per term
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MA060459 | ||
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Master Your Thesis in English 1
Writing is the key priority and the need of utmost importance for all would-be scientists. Science demands not just writing, but good writing. It presupposes the skills to communicate ideas, theories and findings as efficiently and clearly as possible. Science lives and dies by how it is represented in print and a printed material is the final product of scientific endeavour. The primary goal of this course is to prepare master students for wiring, editing, and defending a Master Thesis.
This course is designed to explain how to write chapters of their Thesis through practical examples of good writing taken from the authentic linguistic environment. The course teaches how to overcome certain typical problems in writing a text of a thesis and abounds in useful linguistic assistance. Feedback on students’ texts will constitute the major part of the course. |
Anastasiia Sharapkova | 3 | Extra | MF030003l | |
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Master Your Thesis in English 1 (Term 5B-6)
The Course offers concise and practical guidelines for writing and defending a Master Thesis at Skoltech. The course focuses on the main parts of the Thesis in terms of structure, vocabulary and grammar, and their transformations for a presentation with slides. Students will develop a conscious approach to own writing and presentations through thorough analyses of the best authentic examples combined with intensive writing and editing practice. The ‘process-for-product’ approach teaches the students to write – use (peer) reviewer’s advice – revise/edit – repeat and develop linguistic awareness needed to avoid the typical pitfalls in writing and live presentation.
The Course is offered in two modules which gradually build on the necessary writing and presentation skills. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | MF030003 | |
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Materials Chemistry
The goal of this course is to provide a survey of materials chemistry and their characterization techniques with an emphasis on chemical, electrical, optical and magnetic properties. Further emphasis will be placed application of materials chemistry to energy storage and conversion processes (batteries, fuel- and solar-cells)
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Keith Stevenson, Stanislav Fedotov |
6 | DA060042 | ||
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Materials Selection in Design
This course illustrates the need for a scientific and practical method of selection of appropriate materials in the design for industrial application. It includes the review of the principles of materials science including materials classification, hierarchical structuring, related properties, and performance of different class of materials such as natural materials, metals, ceramics, plastics, cellular solids. Ashby’s material selection algorithm for rational selection of materials for specific applications will be taught here in comprehensive way – analysis of function, objectives and constraints, deducing of performance indices. All the concepts covered in lectures will be practiced by using a commercially available software known as CES EduPack to implement data intensive learning. Individual course projects are aimed to taste the CDIO approach in Materials Selection for a product that must meet the sustainability requirements.
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Svyatoslav Chugunov | 3 | MA030099 | CANCELLED | |
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Mathematical Methods in Engineering and Applied Science (Term 1B-2)
The course introduces students to mathematical methods widely used in modern engineering and applied sciences. It consists of three main parts: 1) Methods of Applied Linear Algebra (solving linear systems, LU, QR, SVD, and other factorizations, Principal Component Analysis, iterative methods, FFT, least squares, pseudo-inverse, etc.); 2) Statistical Methods and Data Analysis (mean, variance, probability; moments, covariance, Gaussian processes; regression, gradient descent; neural networks, machine learning); and 3) Applied Differential Equations (linear and nonlinear ordinary differential equations, stability and bifurcations of solutions, linear and nonlinear partial differential equations, hyperbolicity, characteristics, dispersion, reaction-diffusion phenomena, pattern formation).
The course is introductory by nature, covering a wide range of topics and methods of modern interest in applications. Its theoretical content is informal in style and most of the concepts will be illustrated with problems from engineering, physics, chemistry, and biology using numerical computations in Matlab and Python. The three parts of the course are aimed at: part 1 – using the right language that is crucial for understanding many computational techniques used in engineering; part 2 – learning important tools of analysis of results obtained either by computation or in experiments; and part 3 – learning the nature of key mathematical models that form the foundation of engineering and applied sciences. |
Aslan Kasimov |
63 per term
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MA060352 | ||
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Mathematical Modeling in Biology
The course aims to teach students to quantify biological observations into conceptual models, frame these models in mathematical terms, and analyze these models, both qualitatively and numerically.
It includes considering strategies to choose the relevant variables, parameters and observables, model nature (e.g. discrete vs continuous), modeling technique (e.g. agent-based simulations vs. dynamical system approach), and visualization and interpretation of the results. The following classes of systems will be used as examples: 1. Population models |
Yaroslav Ispolatov | 6 | MA060033 | ||
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Modeling of Multiphase Flows
This is a course into foundations of the Multiphase Flow Modeling.
We consider the basics of the multiphase flow modeling, including the multi-continua approach, the derivation of the multi-phase flow models from first principles (mass and momentum conservation laws) within the multi-fluid approach. Closure relations for suspension rheology and particle settling are discussed. Small scale phenomena are considered: particle migration, and bridging, transition to close packing. We specifically consider multiphase flows in reservoir, fracture and well, as applied to oil production technologies. |
Andrei Osiptsov | 6 | MA060344 | CANCELLED | |
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Models of Sequential Data
In this course, we discuss the forefront of modern research in learning from sequence data. The course takes a walk from the basics of sequence processing to modern deep learning approaches. We aim at covering both fundamental and modern advances in this area not commonly discussed in undergraduate or graduate Machine Learning and Deep Learning classes.
Over multiple weeks, we will investigate how researchers and practitioners use these methods and algorithms for analyzing time-series data, text data, or medical sequences. The course aims to bring all students on the same page. They do not require severe background knowledge. The objective is to provide them with both depth and breadth knowledge of the state-of-the-art in sequence modeling. |
Alexey Zaytsev | 3 | MA030433 | ||
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Modern Wireless Systems - 5G and Beyond
The course covers modern wireless systems, including advanced telecommunication technologies (5G/6G) which form the essential basis for next-generation communication deployment. At the same time the course gives an overview of the opportunities for the mobile communication industry created by cross-industry transformation and traditional business models changes.
During the course the students will consider the following questions: During the course the number of lab sessions will be held with the use of 2G/3G/4G/5G testbenches. As a result of the course, the students will be able to provide a solid understanding of modern wireless communication concepts that satisfy the current telecommunication market needs in enabling next-generation communication models and new services. |
Dmitry Lakontsev | 3 | MA030410 | ||
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Molecular Biology (Term 1B-2)
Molecular biology course is based on learning the principles of replication, recombination, DNA repair. Additionally, replication strategies of phages and viruses will be discussed. Mitosis and meiosis will be described in a context of DNA biosynthesis. Also, the principles of RNA biosynthesis, i.e. transcription and processing, as well as protein biosynthesis, i.e. translation, maturation and transport will be described.
The goal of the course is obtaining a comprehensive knowledge on the structure of DNA and processes of DNA replication, recombination and repair in bacteria and eukaryotes, as well as on replication of phages and viruses. To obtain a detailed knowledge on the processes of transcription, in bacteria and eukaryotes, on the regulation of transcription in bacteria and eukaryotes, on examples of complex networks of transcriptional regulation in bacteria and eukaryotes, on maturation of RNA in eukaryotes, on protein biosynthesis in bacteria and eukaryotes, on the transport of protein in bacteria and eukaryotes. Students activities include: |
Petr Sergiev |
63 per term
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MA060034 | ||
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Molecular Neurobiology
The Molecular Neurobiology course gives students the basics of molecular organization and functional principles of the central nervous system. This is a theoretical course, describing the current vision of how the central nervous system works at the cellular, subcellular, and molecular levels. The course will also introduce current methods used to assess the functional organization of the nervous system at the molecular level, with a particular focus on studies of the human brain. The course will include both the textbook information, as well as recent findings not yet included in textbooks.
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Philipp Khaitovich | 6 | MA060397 | ||
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Numerical Linear Algebra
Numerical linear algebra forms the basis for all modern computational mathematics. It is not possible to develop new large scale algorithms and even use existing ones without knowing it.
In this course I will show, how numerical linear algebra methods and algorithms are used to solve practical problems. Matrix decompositions play the key role in numerical linear algebra. We will study different matrix decompositions in details: what are they, how to compute them efficiently and robustly, and most importantly, how they are applied to the solution of linear systems, eigenvalue problems and data analysis applications. For large-scale problems iterative methods will be described. I will try to highlight recent developments when it is relevant to the current lecture. This course should serve as a basis for other IT Skoltech courses. It will also serve as a first-time place where programming environment and infrastructure is introduced in a consistent manner. |
Ivan Oseledets | 6 | MA060024 | ||
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Organic Geochemistry of Petroleum Systems
The course offers an introduction to the principles and methods of organic geochemistry as applied to conventional and unconventional hydrocarbon systems.
The course covers: the basic chemistry and biogeochemical processes involved in the generation and transformation of organic matter to different types of fossil fuels; the depositional environments that lead to the formation of effective source rocks; the use of individual organic compounds (biomarkers) and their isotopes for exploration and development of hydrocarbon deposits. Specific examples, from published literature, of applying biomarker and their stable isotope compositions in petroleum basin studies will be discussed and evaluated. Lectures are supplemented by two seminars, which will allow students to explore the role of organic matter chemical composition and the thermal history of a sedimentary basin on source rock hydrocarbon potential. Two laboratory sessions will give students hands on experience with analytical chemical procedures for organic matter analysis. A distinctive new feature of the course is the discussion of the latest analytical and instrumental techniques for molecular and stable isotope compound-specific characterisation of biomarkers from source rocks and petroleum. |
Nikolai Pedentchouk | 3 | MA030466 | ||
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Parallel Computing in Mathematical Modeling and Data-Intensive Applications (Term 5B-6)
This interdisciplinary course:
— makes the students familiar with main scientific and engineering applications of modern supercomputers, — explains numerical methods behind the applications and their implementation — discusses efficiency of the common algorithms on modern supercomputer architectures — extends students background in modern processors and supercomputer architectures The applications includes computations fluid dynamics with finite difference and finite volume methods, Lattice Boltzmann and cellular automata, finite elements modeling, molecular simulations, plasma, quantum chemistry, distributed deep learning on multiple computing devices, processing big volumes of data (e.g. large graphs) on distributed systems. Each topic includes a lecture by a lead instructors, invited high-profile guest lecturers and students. Each lecture is devoted to a particular application. Students will form teams to work on projects in one of the application areas and then share their experience with the fellow students at seminar sessions and a final project presentation at the conclusion of the course. |
Sergey Rykovanov, Alexey Vishnyakov |
63 per term
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MA060411 | MOVED FROM T5-6 | |
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Path Integrals and Physics of Open Quantum Systems
The course 'Path integrals and physics of open quantum systems' is devoted to quantum theory of microscopic systems, interacting with reservoir. Basic approach used in this course is the Feynman's path integration technique. We start with describing how this approach can be used as an alternative (to solution of the Schroedinger equation) to study quantum tunnelling of an isolated system. Next, we generalize the technique to describe open quantum systems. We make use of density matrix formalism and Feynman-Vernon influence functinal to study the effects of dissipation on interference and tunneling.
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Konstantin Tikhonov | 3 | DA030442 | CANCELLED | |
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course
describes the intended learning outcomes and how they are assessed. The main bulk of the 81 hours of the course is spent in the actual courses in which |
Dmitry Artamonov | 3 | DG030005 | ||
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Permafrost and Natural Hydrates
This course is about permafrost and natural hydrates. The course is devoted to the consideration of cryogenic-geological conditions of the northern oil and gas provinces of Russia and their influence on the construction and operation of production wells. The course includes permafrost characterization within the main oil and gas fields in the European North and Siberia, including the Arctic zone. The main cryogenic-geological processes occurring in the areas of permafrost propagation are considered. The description of gas and gas hydrate accumulations in permafrost is given. The conditions for the formation and existence of gas and gas hydrate accumulations in permafrost are analyzed. Zoning of the territories of oil and gas provinces on the complexity of geocryological conditions for the development of deposits is carried out. The characteristic of engineering and permafrost studies for the selection of construction sites for producing wells is given. Analyzed the complications arising from the construction and operation of wells in permafrost. Thermal and mechanical interaction of producing wells with permafrost is considered. The behavior of gas hydrate accumulations in permafrost zone during the development of gas and oil fields in the Arctic is analyzed. The impact of global climate change on the stability of wells and ground engineering structures of the oil and gas complex is assessed.
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Evgeny Chuvilin | 3 | MA030343 | ||
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Planning Algorithms in Artificial Intelligence
Planning is the process of deciding which action to take in order to achieve some goals. This course will study planning and decision making under the scope of Artificial Intelligence, that is, we will mostly focus on algorithms that calculate the actions to take by an agent.
The course will cover a wide variety of planning problems, such as discrete planning, path planning, continuous planning, decision-making, planning under uncertainty, learning-based, etc. This diversity corresponds to the main objective of the course: provide a solid understanding to the student to successfully apply different planning techniques into a large variety of problems, including robotics, controls, manufacturing, drug design, computer graphics, and aerospace applications to name a few. The evaluation will consist of problem sets, related to seminar material prepared in class plus a final group project. |
Gonzalo Ferrer | 3 | MA030420 | ||
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Plant Biology
In the past 10-20 years plant biology is at the peak of new discoveries. Advances in DNA sequencing, especially NGS, and the methods of genome transformation, boosted the development of this field. This course will highlight several topics in plant biology, with a focus on the plant genome and the mechanisms leading from the gene(s) to the phenotype. The course will combine lectures and seminars. The seminars will be held in the journal club format, where students will read and then discuss papers reporting important advancements in plant biology.
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Maria Logacheva | 3 | MA030481 | ||
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Postgenomic Technologies for Precision Medicine
This course offers lectures, seminars, and practical classes on implementation of post-genomics multi-omics technologies for medicine. The course is a continuation of the “Biomedical Mass-spectrometry” and “Omics Technologies” courses. The course covers on-going Omics lab research studies in the field of infectious and somatic diseases (including COVID-19 and its health consequences), neurodegenerative diseases (Alzheimer's disease) and various cancers. After an introduction to mass spectrometry (MS), the course will demonstrate how MS-based modern multi-Omics techniques (including those that are being developed at Skoltech) are being used to solve vital medical problems.
Relevant clinical studies and cases will be discussed, including the multiplexed determination of the relative and absolute concentrations of different Omics markers in plasma, tissues, and other patient biofluids, the use of big data analysis to search for disease-specific molecular signatures for diagnostics/prognostics and for developing diagnostics kits. In addition, the visualization of Omics biomarkers on tissue sections by MALDI-MS will be demonstrated by our on-going studies (e.g., on brain tumor). MS-based proteomic and phosphoproteomic analyses of signaling pathways for the dissection tumor pathogenesis will also be demonstrated. A significant part of the course will be devoted to the implementation of bioinformatics and big data analysis tools, including machine learning and artificial intelligence (AI) for clinical multi-omics studies, with demonstrations to show how it helps to improve therapeutic outcomes. By successfully completing this course, students will have acquired a deeper knowledge of how multi-Omics assays are used in medical research, the principals of bioinformatics and big data analysis for discovering prognostic and diagnostic biomarkers, and the generation of specific assays and kits to improve precision medicine. |
Christoph Borchers, Grigoriy Kovalev |
3 | MA030472 | ||
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Practicum in Experimental Physics 1 (Term 2-3)
This course assumes mastering in certain experimental techniques in physics, including a practical work with experimental setups. The course is practically oriented, with small share of lectures. Students will have an opportunity to conduct individual research project and be familiar with unique state-of-the-art equipment.
The work can be continued in Term 4 (other 4 techniques to be chosen), or can be finished in Term 2. For Skoltech-MIPT net program, both Term 2 and Term 4 are obligatory from MIPT side, but can be substituted with other courses in frames of individual MIPT plan. |
Valery Ryazanov |
63 per term
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MA060208 | ||
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Principles of Applied Statistics
Standard courses in mathematical statistics focus on classical statistical methods. However, in practice, modern statistical methods are often used, for example, bootstrap, nonparametric estimation, smoothing based on decomposition in orthogonal bases, methods for reducing dimensionality and sensitivity analysis, etc. Understanding the theory underlying these methods, as well as the ability to apply them in practice, is absolutely necessary for anyone working in mathematical statistics and data analysis.
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Maxim Panov | 3 | MA030416 | ||
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Protein Chemistry and Engineering
Proteins play a central role in the functioning of all living things. Unlike nucleic acids, proteins are extremely diverse in their physicochemical properties and biological functions. This diversity is based on the properties of amino acids, which vary greatly in size, hydrophobicity and charge. Additional sources of structural and functional diversity are various cofactors and post-translational modifications of proteins. During this course, we consider hierarchical levels of protein organization (from amino acid residues to quaternary complexes), as well as the basic principles of protein functioning (enzymes, structural proteins, signaling cascades). This knowledge provides the rationale for protein engineering. In addition, we discuss modern concepts of the origin of life with an emphasis on protein-related issues. Main topics:
Protein structure: Protein functioning: Cofactors and posttranslational modifications: Intracellular protein trafficking: Protein degradation: Protein engineering: Origin of Life: |
Konstantin Lukyanov | 3 | MA030373 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Maxim Fedorov | 3 | DD030020cd | ||
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Qualifying Exam: Engineering Systems
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Clement Fortin | 3 | DD030020es | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Dimitri Pissarenko | 3 | DD030020pe | ||
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Renewable Energy
The course will present a comprehensive study of modern renewable energy resources integration to power systems. Mostly focused on wind and solar power – the main contributors to renewable generation profile – the course will provide with profound technical expertise in the field of planning, grid level behavior, and device-level control of renewable energy sources.
With the falling prices for power electronics devices, there is an exploding grows of grid connected renewable generation all over the world. Being taken for granted by most people, these power sources have quite sophisticated control systems inside them. In this course we will uncover the complex dynamic behavior of the systems, that govern the stable and secure operation of such devices with the main power grid. Solar and wind maximum power point tracking (MPPT) techniques to extract the maximum power from the primary sources, phase-locked loop systems for tight grid connection, doubly fed induction machines for flexible power output – are all the part of the course. |
Petr Vorobev | 6 | MA060201 | ||
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Research Methodology: CDMM Research Seminar (Term 2-4)
This is the main research seminar for the Skoltech Center for Design, Manufacturing and Materials (CDMM). All MSc students either enrolled into the Master Program in Advanced Manufacturing Technologies or PhD students affiliated with CDMM should attend this seminar. The format of the seminar is weekly invited lectures from top scientists in the research fields related to Advanced Manufacturing, Digital Engineering Technologies, and Mechanics and Physics of Advanced Manufacturing will be given.
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Iskander Akhatov |
31 per term
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DG030102dm | ||
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Research Methodology: Computational and Data Science and Engineering (Term 2-3)
A modern researcher needs to have a set of various skills in order to conduct research efficiently. In addition to high level of research skills and understanding of the research environment of one’s particular field, a researcher should be able to manage research-related business processes, be personally effective, have high level of communication and presentation skills, build effective professional relationship with colleagues and effectively manage the career development. The course covers all these topics and implies active interaction between the tutor and students during the classes. In the end of the course each student will be asked to write an essay.
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Maxim Fedorov |
31.5 per term
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DG030102c | CANCELLED | |
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Research Methodology: Space Center Seminar (Term 1B-4)
The seminar will cover current topics in the space domain: latest news, discoveries. Also planned that all PhD students and some Master students will present their research. External lecturers will be invited to focus on the main applications of space technologies: science, telecommunication, navigation and remote sensing. Aspects of space technologies will also be discussed: structures, software, attitude determination and control systems, on board computers, communication system power supply systems and others. The seminar will be offered in English.
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Anton Ivanov |
30.75 per term
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DG030102es | ||
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Research Seminar "Advanced Materials Science" (Term 2-4)
This is the main research seminar of the Skoltech Center for Electrochemical Energy Storage and Materials Science Education program featuring presentations of young researchers: MSc students, PhD students, postdocs. Every MSc and PhD student of Materials Science program should deliver at least one presentation per two years. The range of topics is broad and includes any aspects of materials science and engineering.
Please see the seminar webpage at http://crei.skoltech.ru/cee/education/wednesday-scientific-seminar/ |
Keith Stevenson |
1.50.5 per term
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DG030302i | ||
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Research seminar "Cluster Integrable Systems and Supersymmetric Gauge Theories" (Term 1B-2)
This research seminar will be devoted to the study of N=2 supersymmetric gauge theories
and related topics. It turns out that comparing to the N=1 theories, N=2 supersymmetry allows to compute much more quantities. In particular, low-energy effective action can be described in terms of single function, prepotential. Seiberg-Witten solution of the N=2 theory gives explicit description of the prepotential in terms of periods of some meromorphic differential on algebraic curve. It turns out that this description is deeply related to classical integrable systems. This will be the working seminar where we are going to discuss some topics related to Seiberg-Witten theory in 4D and 5D: partition functions, relation to the integrable systems and their deautonomizations (isomonodromic deformations). On the integrable systems side we will consider cluster integrable systems (like relativistic Toda chains), which come from the dimer models or from double Bruhat cells in Poisson-Lie groups. We are going to discuss their Lax representation, discrete and continuous flows, relation to the dimer models, etc. We expect some talks given by participants. |
Pavlo Gavrylenko |
63 per term
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MA060461 | ||
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Research Seminar "Energy Systems and Technologies" (Term 2-4)
This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8).
Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Evaluation. Filling in the Online feedback form. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. |
Elena Gryazina |
31 per term
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MA030489 | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1B-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and their theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, etc.
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Pavlo Gavrylenko |
61.5 per term
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DG060268 | ||
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Reservoir Rock Characterization
The course provides conclusive theoretical knowledge in reservoir characterization and laboratory measurements of the rock properties. During the course the students will learn the key rock parameters, defining the reservoir geological and recoverable reserves, well design and completion, field development strategy. Particular emphasis is made on the genesis and relationships of the rock properties as well as their relevance for well logging data interpretation and geological modeling.
The course consists of lectures and seminars, including video demonstration of laboratory equipment, used for measuring rock properties. The participants will be acquainted with every stage of reservoir rock study, starting from coring while drilling, core handling at the well site, its further processing, sampling, transportation and preparation for laboratory tests. Further the students will learn in details routine and special laboratory tests (SCAL). Finally, the students will study how to design a laboratory program, acknowledging geology and rock properties of a reservoir. |
Alexei Tchistiakov | 3 | MA030346 | ||
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RNA Biology
This course is devoted to the knowledge on the structures of RNA and RNA-protein complexes as well as their functioning in cells. The aim of this course is to provide an explanation of fundamental mechanisms such as translation, splicing and gene expression regulation based on the structural viewpoint. Thus the role of RNA in the maintenance of cell identity and cell metabolism will be defined. By focusing on modern techniques for RNA and RNA-protein structure and RNA modifications analysis, students will get acquainted with the approaches to study RNA input into cellular processes in vitro and in vivo.
The students will apply obtained knowledge and skills in presentations and a written exam. An examination commission, consisting of CLS faculty and of invited members, will conduct final evaluation of the overall product design completeness, quality of the results achieved, and of the presentations delivered. Prerequisites: Molecular Biology course |
Timofei Zatsepin | 3 | MA030081 | CANCELLED | |
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Robotics
Robotics became the key driving force for the new industry and hundreds of thousands of industrial robots are installed annually. Students of the course will learn how to design innovative multi-DOFs, mobile, and flying robots. They will learn how to program Arduino, STM32, ESP microcontrollers, digital filters, and to write the software for controlling the servo and DC motors. Topics will include Forward and Inverse kinematics of 6-DoF robots, Jacobians, Robot dynamics, Trajectory planning, PID control. Python for robots and Robot Operating System (ROS) Lectures will help to develop strong skills in robot programming. Students will be taught the unique robotic technologies, such as, inverse Delta robot DeltaTouch and touch-sensitive soft robotic gripper, both are designed in ISR Laboratory. Students will also have a chance to develop autonomous mobile robots for Eurobot International Robotics Competition. Students will have the access to world’s top-level robotic solutions, e.g. KUKA IIWA, Universal Robots, Fanuc Delta Robot, DJI drones. The invited speakers will be the leaders of startups founded by ISR Lab. students, such as, Native Robotics, Tsuru Robotics, Sizolution and PhD students from Italian Institute of Technology, University of Southern Denmark, Tohoku University, etc.
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Dzmitry Tsetserukou | 6 | MA060050 | ||
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Selected Topics in Energy: Physical, Chemical and Geophysical Challenges (Term 2-4)
The course provides an introduction to the modern topics related to fundamentals of exploration of energy resources, energy generation, storage, conversion and use. It identifies the corresponding practical challenges to be addressed at the fundamental research level and familiarizes the students with the state-of-the-art approaches, methods and techniques in use in related scientific areas. The course seeks to emphasize and maintain interdisciplinary nature of the energy-related topics, in particular, combination of micro- and macroscopic approaches of geophysics, mechanics and chemistry in hydrocarbon exploration and development, relation between the physical and chemical processes of energy generation and conversion, integration of physical, chemical and mechanical approaches to perspective materials (physical and chemical synthesis, micro- and macroscopic characterization, structure-property relations, etc.) and related theoretical methodologies. These interdisciplinary links are mostly demonstrated by horizontal knowledge exchange among the students reporting and discussing practical examples from their own research field or from modern review or research publications. Topical lectures are included for further exploration of these links. The secondary aim of the course is the development of presentation skills (oral and writing), as well as scientific peer-review experience. The seminar format chosen for most activities allows students free exchange of knowledge and ideas, broader vision of their research projects and methodologies, better assessments of their own research skills and demands for further education.
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Alexei Buchachenko |
62 per term
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DG060106 | ||
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Spacecraft Dynamics and Control
This course surveys basic concepts and computational fundamentals of astrodynamics and then proceeds with the principles of spacecraft attitude determination and control. The emphasis throughout the course is made on solving real-world engineering problems and analyzing up-to-date misions. Orbital and rotational dynamics of spacecraft are discussed and simulated under a variety of environmental conditions, along with the realistic constraints imposed by available hardware.
The first part of the course is focused on the orbital dynamics of spacecraft and discusses main principles of how the orbits of satellites or trajectories of spacecraft are formed due to environmental factors and how they are designed, when a space mission is planned. The second part of the course shows a few methods of nonlinear dynamics identification and control through the example of attitude control systems. The students will learn how the attitude control systems are modeled and designed, and what sensors, actuators and algorithms are used. |
6 | MA060379 | CANCELLED | ||
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Startup Workshop
Startup Workshop (SUW) is the 3-credit E&I course designed to accelerate the Skoltech student/faculty/researcher teams developed and inspired by the Innovation Workshop and similar project-based E&I courses (IW, SFW, or TEF), though any Skoltech team is welcome to join through the mechanism of competitive selection. SUW course is extremely practical and pragmatic as its whole and only point is the preparation of the project application for the startup financing coming from two core Russian entrepreneurial infrustructural organizations: Skolkovo Foundation (SkF) and FASIE.
Despite such formal learning objective may look too narrow and mundane, it allows distinguishing the SUW course in two unique ways: — 1) building the SkF application that is well-grounded and properly structured, is an intensive exercise requiring major learning/experimenting/prototyping. The team that passed the SUW will be ready to face each and every venture investor of the world; — 2) as SUW teams enter the formal path of Skolkovo startup, they will obtain an intensive help from the Skoltech Dept of Business Devt, that will provide not only mentoring, but also some minor competitive financing. SUW pushes teams through the preparation of the SkF/FASIE application that consists of 6 building blocks: 1) problem validation, 2) product/technology description and validation, 3) competitive analysis and market assessment, 4) commercialization plan, 5) team and roles, 6) integrative 3-yr plan. SUW is quite intensive: it starts well before the Term 2 with the competitive selection and requires serious work each week to produce the graded presentation. |
Dmitry Kulish, Alexey Nikolaev |
3 | E&I | MC030025 | |
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Statistical Mechanics, Percolation Theory and Conformal Invariance (Term 1B-2)
This is a course on rigorous results in statistical mechanics, random fields and percolation theory. Some of it will be dedicated to the theory of phase transitions, uniqueness or non-uniqueness of the lattice Gibbs fields. We will also study the models at the criticality, where one hopes to find (in dimension 2) the onset of conformal invariance. We will see that it is indeed the case for the percolation and the Ising model.
The topics will include: Crossing probabilities as a characteristic of sub-, super- and at- criticality. Critical percolation and its power-law behavior. The Russo-Seymour-Welsh theory of crossing probabilities – a cornerstone of critical percolation Cardy’s formula for crossing probabilities Parafermionic observables and S. Smirnov theory Conformal invariance of two-dimensional percolation a la Khristoforov. Conformal invariance of two-dimensional Ising model O(N)-symmetric models Continuous symmetry in 2D systems: The Mermin–Wagner Theorem and the absence of Goldstone bosons. The Berezinskii–Kosterlitz–Thouless transition Reflection Positivity and the chessboard estimates in statistical mechanics Infrared bounds and breaking of continuous symmetry in 3D |
Semen Shlosman |
63 per term
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MA060465 | ||
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Statistical Natural Language Processing
This course gives introductory insights into methods that are used in natural language processing systems. This is an introductory NLP course dedicated to classic algorithms and models for NLP yet with the coverage of some more recent neural models. The course is largely based on the Jurafsky&Martin textbook, but also features lectures on graph-based models for NLP and data annotation for NLP.
If you would like to get a course on purely "modern" neural NLP methods similar to Stanford's CS224n, then you shall enroll in the "Neural Natural Language Processing" course at Skoltech. Thus, given a very broad scope of NLP, we decided to split the sheer volume of material into these two complementary 3 credit courses. Goals of this course: – understand methods for language processing in detail |
Alexander Panchenko | 3 | MA030131 | ||
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Stochastic Methods in Mathematical Modeling
Stochastic processes play an important role in natural sciences, computational theory as well as in sampling and synthetic data generation for machine learning. The course aims to cover basic methods of stochastic modelling, such as: Monte-Carlo methods, the modelling of scale-free phenomena as well as stochastic optimisation approaches.
The first part of the course provides an introduction to the methods of description and generation of randomness. The main idea is to establish a firm ground for more advanced topics and to help students feel comfortable with advanced machine learning courses. A special emphasis is put on the ubiquitous scale-free and non-gaussian stochastic processes also known as anomalous diffusion. Different causes of these processes will be discussed with examples such as practically important class of first-passage problems. The second half of the course will deal with Monte-Carlo algorithms, inference and learning, classical random network theory, Markov decision processes and stochastic optimal control. |
Vladimir Palyulin | 6 | MA060363 | ||
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Structural Analysis and Design
The Structural analysis and Design course gives students basics of strength analysis and design of structural members. Theoretical sections include introductions into stress and strain theories, failure criteria, elasticity theory, nonlinear material behavior and analysis of tension, compression, bending and shear structural members. The students work with Abaqus software to develop simple finite element models validated by analytical solutions. Typical structural design problems are considered in the framework of course projects. Students welcome to propose the problem statements for the course projects in order to support their MS or PhD projects.
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Ivan Sergeichev | 3 | MA030067 | ||
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Symmetric Functions (Term 1B-2)
The theory of symmetric functions has numerous applications in various domains of mathematics and mathematical physics. At the beginning of the course, standard material will be presented, and then we will move on to more advanced topics.
Tentative program: The algebra Sym of symmetric functions. Generators of Sym. The scalar product, involution map, and Hopf algebra structure. Schur functions, skew Schur functions. Combinatorial formula. Cauchy identity and dual Cauchy identity. Jacobi-Trudi formula and its dual version. |
Grigori Olshanski |
63 per term
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MA060458 | ||
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Systems Engineering
The course introduces students to the fundamentals of systems engineering as an interdisciplinary approach and means to enable the realization of successful systems, as defined by the International Council of Systems Engineering.
The course covers the entire spectrum of the lifecycle management of a system, encompassing conceptual design, design, implementation, assembly-Integration and test (AIT), operations and disposal of systems. Being a foundational course for the Space and Engineering Systems students of Skoltech, the course discusses many applications of systems engineering including some parts of space systems engineering . The course also discusses systems architecture principles. The Systems Engineering course follows the systems engineering V-model as an educational guideline. The course includes a design project that is conducted throughout the term. |
Clement Fortin | 6 | MA060023 | ||
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Technology Entrepreneurship: Seminar (Term 1B-2)
The course is designed to help you to master practical skills of technology entrepreneurship and to accelerate your startup projects up to “external support/funding ready” level. It is intended for students: (1) interested in new tech venture creation and technology entrepreneurship; (2) having their own projects/ideas in development; (3) planning startup contest participation, pitch to investors, applying to Skolkovo Foundation, “Bortnik Fund”, STRIP, accelerator/incubator program, etc. The startup project concept may be in the experimental mode, or further along in its evolution such as seeking customers or pilot tests.
The course will be conducted as practical and hands-on lab. We will not study entrepreneurship as a theoretical subject through external cases or papers. The material for learning comes directly from the class projects and the issues faced by students in converting their projects into successful ventures. This assures projects accelerated development and creates a highly dynamic environment for teaching where the faculty is a facilitator, mentor, tracker, and lecturer at the same time. Within the course project teams are expected to develop and deliver various presentations on selected aspects of their business idea. Each class member is expected to contribute actively to the discussions and presentation critiques. The course subject areas represent “golden standard” for tech startups: (1) Problem and Market Need; (2) Product and Technology; (3) Market evaluation; (4) Business Model; (5) Startup Team; (6) R&D/Marketing/Sales/Funding plan; (7) Storytelling and Pitching. Upon the course completion, you and your team will |
Alexey Nikolaev |
31.5 per term
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E&I | MC030029 | |
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Theoretical Methods of Deep Learning
Deep Learning (DL) is a highly promising and popular applied science that, at present, is poorly understood theoretically. We know that neural networks work well, but cannot fully explain why. Nevertheless, in the last few years, there has been a rapid growth of publications that shed light on the new mathematics underlying DL, and we see now many interesting connections between DL and other fields such as approximation theory, differential equations, information theory, random matrix theory and statistical physics. This course aims to introduce students to these cutting-edge developments.
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Dmitry Yarotsky | 3 | MA030327 | ||
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Thermal Petrophysics and Geothermy
The course presents theoretical and experimental background of modern thermal petrophysics and geothermics in application to solution of actual problems of fundamental and applied geophysics and geology of unconventional hydrocarbon resources. The recent essential evolution in experimental and theoretical basis of the thermal petrophysics and oil&gas geothermics is described. Qualitatively new possibilities in prospecting, exploration and development of unconventional hydrocarbon fields based on advanced theoretical and experimental basis of thermal petrophysics and geothermics are shown. Important peculiarities in applications of new methods of thermal petrophysics and geothermics to heavy oil fields and shale oil fields are illustrated. Necessity of wide implementation of new technologies of thermal petrophysics and geothermics are demonstrated on examples of their applications for investigations of many reservoirs with unconventional resources of hydrocarbons. It is shown that cardinal changes in effectiveness of thermal petrophysics and geothermics are revealed due to development of optical scanning and continuous thermal core logging technologies. Significant improvements of reliability of basin and petroleum system modeling as well as hydrodynamic modeling are demonstrated as new methods of thermal petrophysics and geothermics became obligatory components of prospecting, exploration and development of unconventional resource fields.
As a result, students will know a modern basis and highly effective methods and equipment of thermal petrophysics and geothermics to apply the new techniques for prospecting, exploration and development of unconventional hydrocarbon fields. |
Yuri Popov | 6 | DA060295 | ||
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory 6 credits component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 | ||
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Topics in Neurobiology Seminar
A research paper-based course.
Overview of current research relating to various ‘hot topics’ in neurobiology and discussion of current research articles on the subject. Analysis of experiments and research described in scientific papers are presented by students and critically discussed by the class led by the instructor. Novel methods in neurobiology – optogenetics, molecular magneto technique, transparent tissues imaging will be discussed in depth. Topics include mapping of the brain and behavior, optogenetic manipulation of memory engrams, mouse models of Alzheimer disease, synaptic plasticity, dendritic spines morphology, pathology, and neurodegenerative diseases. |
Dmitry Artamonov | 3 | MA030104 | CANCELLED | |
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Transport in Mesoscopic Systems
The course aims to provide introduction to a modern direction of the solid-state physics, devoted to studying charge transport (charge currents) in mesoscopic structures. Mesoscopic structures are intermediate between micro- and macroscopic systems; in our context, this name refers to systems with many electron in which mechanics (in particular, quantum mechanics) of single electrons is still important. The course consists of two parts, devoted to normal (i.e., non-superconducting) and superconducting systems. A number of these systems form a basis for nanoelectronics devices. The course assumes participation of students interested in both experimental and theoretical aspects of mesoscopic research.
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Yakov Fominov | 6 | MA060217 |
Winter
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
|---|---|---|---|---|---|
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Pedagogy of Higher Education
The course offers an introduction to facilitating learning in higher education for PhD students asked to act as teaching assistants. The course content focuses on high resolution constructive alignment of learning outcomes with learning activities and assessment strategies. Learning outcomes for a course are elaborated into separate activities and assignments for students. Learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. Participants in the course will therefore be required to plan and design effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments. The course therefore emphasizes communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course explores ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching assistantships or their supervisory activities to come. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson | 3 | DG030025 |
Term 3
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
|---|---|---|---|---|---|
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Academic Communication: Preparatory English for Phd Exam (Term 3-4)
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. The course is aligned with the NATURE MASTERCLASS available to Skoltech researchers, so you will be able to benefit from professional recommendations of the Nature experts regarding the structure and contents of a publication, and constructive feedback from your Instructor on the language of your materials. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities, to be performed in class and at home, individually and in teams. By the end of the course, successful participants will – know the rules and conventions of research paper writing, including structure, style, grammar and vocabulary; – improve their academic communication skills, such as active listening, spontaneous and rehearsed speaking/ presentation, reading and writing within a given academic genre; – have experience in writing, editing, peer-reviewing and presenting research results. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | DF030029 | |
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Academic Writing Essentials (Term 3-4)
With the growing demands for every scientist to publish and not to perish, the quality of academic writing is of utmost importance. Successful writing presupposes the skills to communicate ideas, theories and findings as efficiently and clearly as possible. The way ideas are communicated is different in Russian and English Academic discourse. The course will discuss successful strategies and typical tactics to communicate science in English.
The aim of the course is to help the students plan the written work, understand its major parts, use the rhetorical devices, and master the linguistic repertoire appropriate in biological academic discourse. The integrative approach unites the top-down and the bottom-up ones. The general logic as well as the minute linguistic devices for presenting, advancing, and reformulating the argumentation will be given. The course teaches how to write, revise and edit your own work in a lingua franca of modern science. The course will familiarize the students with major problems the Russian authors have in the English formal writing as well as the ways to overcome them. Extensive writing, listening to lectures, self- and peer- editing and getting feedback from the lecturer will provide grounds for future autonomous writing in the discipline of biology ( including papers and a Master Thesis). |
Anastasiia Sharapkova |
31.5 per term
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Extra | MF030002l | |
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Academic Writing Essentials (Term 3-4)
Academic writing skills are necessary for effective research, innovation, and educational activities in a multinational setting. The aim of the course is to provide guidelines and strategies for writing academic texts, focusing on relevant aspects of grammar, vocabulary, and style. The course includes analysis and practice of various forms of scientific and technical writing, and builds writing skills from sentences to paragraph structure, from summary to abstract, and lays the foundations for writing scientific papers and Master Thesis.
Modern science is, for most purposes, a collective collaborative effort, so the course is designed to promote individual and group responsibility by providing mutually related and time-dependent tasks, such as peer review. The course is writing-intensive with ample opportunity to practice editing and peer-reviewing. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | MF030002 | |
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Advanced Drilling and Completion Technologies
Course will cover basic and advanced drilling and completion technologies during well planning and execution.
Planning phase includes basics of well design: selection of trajectory, casing design, drilling fluid selection, drilling string design, bottom hole assembly (drilling bits, rotary steerable systems, mud motors, measured while drilling and logging while drilling tools) selection, cementing design, lower completion and upper completion design, wellheads and x-mass tree selection, drilling rig selection, etc. Execution phase covers techniques of directional drilling, hole cleaning, casing running, cementing operation, completion running and emergency situations prevention and recovery while drilling (well control, stuck pipe, etc.). Moreover, the course will cover basics of the offshore drilling and completion. |
Kirill Bogachev | 3 | MA030347 | CANCELLED | |
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Advanced Engineering: Thermal Spray Coatings
Thermal spray technology provides a cost-effective functional surface solution for many applications requiring resistance to wear, heat, and corrosion. This practically-oriented course is intended to familiarize graduate students with an understanding of thermal spray processing science and front-line research topics, with attention to latest development and innovations in the field. The second purpose of this interdisciplinary course is to give the students technological/engineering perspectives of thermal spray applications and practice.
Students’ key learning objectives: Develop knowledge and specific hands-on skills in thermal spray processing of materials; Perform spraying of single- and multi-phase compositions, ceramics, metal-matrix, and functionally gradient materials; Carry out physical properties evaluations of sprayed deposits and assessing coating micro structural features using characterization methods and analysis tools; Build finite element models of spraying nozzles and perform computational fluid dynamic (CFD) analysis to optimize spraying criteria and find optimal process parameters; Use and practice modern principles of advanced manufacturing technologies by performing computer-aided manufacturing (CAM) tasks. |
Dmitriy Dzhurinskiy | 3 | MA030454 | ||
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Advanced Statistical Methods
This course introduces the main notions, approaches, and methods of nonparametric statistics. The main topics include smoothing and regularization, model selection and parameter tuning, structural inference, efficiency and rate efficiency, local and sieve parametric approaches. The study is mainly limited to regression and density models. The topics of this course form an essential basis for working with complex data structures using modern statistical tools.
Course structure: lectures, seminars, exam. |
Vladimir Spokoiny | 3 | MA030132 | ||
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Advanced Topics in Bioinformatics and Genomics 1
The first part of the “Advanced topics in bioinformatics and genomics” is dedicated to structure, function, and evolution of proteins and RNAs. More specifically, the course consists of distinct four parts: (1) Structural Bioinformatics, (2) Molecular Evolution, (3) Evolutionary Genomics, and (4) Quantitative Genetics. The aim of the course is to shortly recall Ph.D. students the fundamentals of the corresponding fields of science followed by in-depth discussions/seminars on some advanced topics. The sessions will be accomplished mostly through the discussion of the classic or recently published papers; however, practical tasks could also take place.
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Dmitry Ivankov | 3 | DA030438 | ||
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Advanced Topics in Cell and Molecular Biology 1
The course showcases Skoltech Life Sciences faculty whose research falls under wet biology. Each faculty will give four lectures presenting their own work and, more broadly, the state-of-the-art in his/her area of expertise. The lectures will be followed by Q&A sessions and are expected to foster collaborations between different faculty and grad students and provide the students with a broad view of research conducted by the faculty. This course (or the sister course on advanced topics in bioinformatics) is required for PhD students but may be taken by Masters students looking for a laboratory to perform a thesis project. To fulfil curricular requirements this three-credit module may be combined with another advanced topics in cell and molecular biology module or with a bioinformatics module.
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Konstantin Severinov | 3 | DA030435 | ||
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Applied Geomechanics
This course covers various aspects of experimental geomechanics and is divided into three parts: lectures, seminars and laboratory work in SCHR Geomechanical laboratory. The lectures provide an introduction into the basic principles of measuring the physical characteristics of rocks during geomechanical testing in the laboratory. Seminars demonstrate examples of geomechanical parameters calculations based on the results of laboratory testing using MS Excel. Usually, oil and gas companies collect rock cores, while drilling the wells and afterwards send these cores to various laboratories to determine the physical characteristics of rocks through comprehensive geomechanical testing. Knowledge of the physical parameters of rocks based on the results of laboratory testing is extremely important for the proper design and development of the most efficient strategy of hydrocarbon recovery in the field conditions.
The final part of the course is to take place in the SCHR laboratory. If the attendance the geomechanical laboratory happens to be restricted again due to COVID-19, then students will be shown videos taken in the laboratory during rock testing. We intend to demonstrate conventional geomechanical testing by stressing the rock samples to their failures under confining pressure, simulating underground conditions, as well as hydraulic fracturing of the rock samples by injecting a high-pressure fluid into the rock. Students will be given the results of the rock tests for their homework, they will have to calculate rock parameters, and then present their results during the project defense. Students should be considered to have passed the final exams of Applied Geomechanics course, if they demonstrate their ability (i) to understand the basics of various measurements in the laboratory; (ii) to make calculations of rock parameters based on laboratory data; (iii) to estimate the accuracy of rock parameters measurements in laboratory. |
Sergey Stanchits | 6 | DA060190 | ||
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Basic Molecular Biology Techniques
The purpose of this course is to provide students with the opportunity to obtain and develop the basic set of skills needed to be successful in a molecular biology laboratory. The course consists of hands-on laboratory work, as well as lectures from course instructors. Students without any significant background in the biological sciences should be advised that additional reading outside of the scheduled classes may be necessary to maximize classroom success (instructors are happy to provide resources at the students’ request).
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Svetlana Dubiley | 6 | MA060022 | ||
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Biostatistics
This introductory course to statistics and probability theory is modeled as an extension of a traditional university Statistics course and Advanced Placement Course in Statistics to a broader spectrum of topics, while keeping the spirit of quantitative discourse applied to real-life problems. The material is offered in 5 consecutive modules (see Course Outline below), each containing a lecture, a discussion section, and a practicum. For practical exercises we use R programming language and R-Studio software. However, this course is focused on statistics rather than R; therefore, each practicum is designed with the purpose to demonstrate and reinforce understanding of concepts introduced in the lecture rather than to provide a training in R.
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Dmitri Pervouchine | 6 | MA060036 | ||
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Business Communication
Business Communication is an intensive hands on, practical course, designed to provide Skoltech students with the set of skills needed to effectively communicate with others – their classmates, working teams, professors and any audiences inside and outside of Skoltech. The course learning outcomes correspond directly with the Group 3 of Skoltech learning outcomes – “Relating to Others – Communication and Collaboration”. The course will show students the secrets and technologies to becoming confident when speaking in public – developing the skills they will be able to use throughout their career and their life. In a highly interactive, informative and supportive manner through in-class activities, games and simulations the course will enable students to: Speak with confidence and overcome their nervousness; Establish rapport with any audience; Present their message in a clear, concise, and engaging manner; Successfully manage impression they make onto audience; Create—and repurpose—presentations quickly and efficiently; Make successful and memorable pitch; Sharpen the story they want to tell; Use confidently body language and movement, strengthening their speech; Respond to questions and comments without getting flustered; Gain people’s attention, respect, and cooperation.
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Maxim Kiselev | 3 | E&I | MC030014 | |
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Carbon Nanomaterials
The course covers the subject of carbon nanomaterials (fullerenes, nanodiamond, nanotubes, and graphene). The history of carbon compounds since antiquity till our days starting from charcoal to carbon nanotubes and graphene will be reviewed. The students will have opportunity to synthesize carbon nanotubes, graphene and nanodiamond (by CVD methods), to observe the produced carbon nanomaterials in transmission (TEM) and scanning (SEM) electron microscopes as well as by atomic force (AFM) microscope and to their study optical and electrical properties.
Totally 32 hours of lectures, 12 hours of exercises and 4 hours of discussion work. During the courses each student is supposed to give a short presentation (15 min) on a selected topic, to write an essay on other selected topic and to prepare an exercise report. |
Albert Nasibulin | 6 | MA060044 | ||
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Composite Materials and Structures
This course aims to provide knowledge about manufacturing, properties, and contemporary problems in composite materials. The emphasis is on the practical applications, theoretical background, and the use of composite materials in industry. The course cuts across several domains, covering mechanics of materials, design, manufacturing, and in service issues:
• Introduction: What is a composite? Classification. Metals vs composites, advantages and disadvantages. Applications in industry. Participants will learn fundamentals of these areas through active participation in teamwork. The course will provide practical knowledge on applications of composite materials in aerospace and mechanical engineering. |
Sergey Abaimov | 6 | MA060241 | ||
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Computational Materials Science Seminar (Term 3-4)
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov |
1.50.75 per term
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MA030430i | ||
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Control Systems Engineering
The course focuses on dynamic systems, and their control. Such systems evolve with time and have inputs, disturbance, and outputs. One can find examples of dynamic systems in everyday life, for examples, automobiles, aircrafts, cranes, electrical circuits, fluid flow.
You will analyze the response of these systems to input. Students will learn how to control system through feedback to ensure desirable dynamic properties (performance, stability). The practice will include work with an industrial, a humanoid, a mobile, and a telepresence robot. |
Dzmitry Tsetserukou, Seyed Hassan Zabihifar |
6 | MA060083 | ||
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Digital Certification of Composite Structures
The course introduces methods of conformity assessment of composite structures based on virtual testing. Basics of continuum damage mechanics and progressive failure models are discussed to be applied for finite element simulation of certification tests of the composite structures. Approaches to calculation and justification of safety factors and design material properties are considered for composite structures mostly related to civil engineering and infrastructure. The material model identification and finite element modeling are performed for virtual testing of composite sub-components. The models are validated by results of full-scale laboratory tests of the sub-components. As the introduction to the certification framework, the general conformity assessment procedures are discussed focusing to examples of typical aircraft composite structures. Abaqus FEA software is used for simulation.
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Ivan Sergeichev | 3 | MA030357 | ||
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English
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research and follows the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, last of three weeks and take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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Entrepreneurial Marketing and Commercialization
Students will learn to find customers, create brands and build a good reputation for their startup, commercialize their projects and inventions through different sales channels, and build and develop enduring relationships with their customers.
Course participants are also prepared to build criteria for evaluating commercialization alternatives, gathering data and analyzing market information, defining brand identity, strategy, and the necessary entrepreneurial skills needed to develop brand's value and manage a viable business. |
Alexander Chekanov | 3 | E&I | MC030445 | |
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Experimental Design in Biology
Reliable phenotypic data at
* the ‘macro’ level such as plant’s fitness, flowering time, bacterial colony growth or * the ‘micro’ level such as cellular response or pathogen colonization within tissues or * the ‘molecular’ level such as gene expression levels or metabolite profiling; are required for in-depth genetic studies. Due to the unique particularity to possibly replicate the same genotype in time and space, the phenotypic data of plants and microbes can be analyzed with strong power and accounting for variability. The design of experiments is all about learning as much as possible from the smallest amount of data. During this course, the fundamentals of determining the effects of given factors or covariates on a phenotypic trait will be given. This includes how to evaluate controlled and uncontrolled variability using the appropriate experimental design. The course is mostly practical-driven and aims to provide the skills to use these methodologies in future professional activities. |
Laurent Gentzbittel | 3 | MA030480 | MOVED FROM T2 | |
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Fabrication Technology of Nanodevices
The course concerns the fundamental and practical aspects of fabrication technologies widely used for the fabrication of nanoscale devices. The course starts with the introduction of cleanroom environment, code of practise and safety for operation in Nanofabrication centres. There are discussed a range of technologies and methods: UV and Electron Beam lithographies, wet and dry etching, thin film deposition, thermal annealing, controllable oxidation and ion beam implantation, metrology of nanoscale devices. An introduction to chemicals used for fabrication and safety operation is given. Finally, examples of fabrication of devices are discussed. Students will have a chance to learn practical operation on some equipment.
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Vladimir Antonov | 6 | MA060311 | ||
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Finite Element Analysis
The course is intended to give basic knowledge and skills for finite element analysis method and modelling. The course introduces basics of FE theory and fundamentals. Static stress analysis, structural stability, crack propagation analysis, extended finite element method (X-FEM), explicit dynamic analysis, heat transfer and thermal-stress analysis and sub-modeling are discussed and experienced within modelling and solution of engineering problems. Application of constitutive material models and equations of state are сonsidered. Abaqus FEA software is used for development of models, analysis and post-processing. MS and PhD projects are supported in the framework of the course via individual course projects. Therefore, students are wellcome to participate for the problem statement for the course projects.
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Ivan Sergeichev | 6 | MA060355 | ||
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Foundations of Multiscale Modeling: Kinetics
The course is devoted to fundamental principles of modelling of kinetic processes at different time and space scales. The basic concepts are introduced, along with the theoretical and numerical techniques, which application to practical problems is illustrated. The course starts with the description of molecular kinetics in fluids, colloidal and polymer solutions; applications for molecular machines and nano-robotics is considered. Langevin and Fokker-Planck equations are introduced, supplemented with the theoretical and numerical tools of their solution.
Next, the Boltzmann kinetic equation is analyzed. An application of the fundamental theoretical and numerical techniques, such as Grad and Chapman-Enskog methods, Lattice Boltzmann and Direct Simulation Monte Carlo is illustrated. Derivation of transport coefficients and hydrodynamics equations, including these for dissipative fluids, is given. Green-Kubo relations as an alternative method for practical computation of transport coefficients is presented and compared with other methods. Based on the Boltzmann equation, the theory of aggregation-fragmentation kinetics is developed, leading to the generalized Smoluchowski equations. The basic concepts like Scaling, Generation functions, etc. are introduced for theoretical analysis and Gillespe algorithm and fast solvers for practical computations. In the rest of the course the above theoretical and numerical techniques are illustrated for the following applications: Surface growth, Phase transition kinetics, Random sequential adsorption, Nucleation & Growth and Nano-tribology. Theory of Active Matter, Traffic Models, Socio-dynamics and Complex Networks dynamics are also considered. The knowledge of undergraduate mathematics – the basics of calculus, linear algebra and probability theory, as well as reasonable skills in Matlab and Python are needed. A familiarity with the basic open source software is desirable. |
Nikolay Brilliantov | 6 | MA060326 | ||
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Fundamentals of Additive Technologies
Additive manufacturing (AM), also called 3D printing, has become an extremely promising technology for Industry 4.0 nowadays. Unlike traditional manufacturing processes such as welding, milling, and melting that involve multi-stage processing and treatments, AM allows creating products with a new level of performance and shapes in a single process.
Moreover, this approach allows production prototypes and functional parts rapidly and leads to reducing costs and risks. Another crucial advantage of additive technologies (AT) is the unprecedented design flexibility that lets us create samples of high quality based on different materials such as metals, alloys, ceramics, polymers, composite materials, etc. The main goal of this course is to represent the fundamental basis of different AT to the students. In this course, a wide range of questions will be addressed, beginning from the process of chain and designing the structures up to various 3D printing technologies, materials and process parameters, benefits and drawbacks of AM approaches will be considered. During laboratory class, we will get acquainted with the AT on various printing machines. Students will be able to create their own models, print them in metals, ceramics, and polymers, and analyze the properties of the final samples. During this course, a complete cycle of production of samples using various 3D printing techniques will be explored both theoretically and practically. |
Igor Shishkovsky | 6 | MA060243 | ||
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Gas Recovery and Methane Hydrates
Natural gases characterization of the gas and gas-condensate fields. Traditional and non-conventional gas resources. Overview of technological complications (flow assurance) in gas production at different stages of field development.
Phase diagrams of hydrocarbon systems including water. General characteristics of phase transformations during reservoir development. A moisture content of natural gas. Gas hydrates: basic physical and chemical properties. Two-phase and three-phase equilibria. Gas hydrates as a technological complication in gas production. Thermodynamic (methanol and MEG) and low-dosage (kinetic and anti-agglomerant) inhibitors. Permafrost at northern gas fields: general characteristic, ice content, thermophysical and mechanical properties of frozen and thawed rocks. Wells and well clusters. Thawing and reverse freezing of rocks around the producing well. Simulation of the thermal interaction of well and permafrost rocks. Thermal regime of the operating well. Gas gathering systems. Technological complications in the operation of infield systems. Gas hydrate control. Gas gathering systems at the late stages of field development (water accumulations, ice formation, sand, scales). The main technological processes of gas treatment in field conditions (general overview). Dehydration of lean gases. Adsorption method of dehydration. Adsorbents and their choice. Technological schemes of adsorption dehydration. Absorption method of dehydration.Glycols as absorbents.Technological schemes of absorption dehydration. Low-temperature processes of gas treatment at gas-condensate fields. Isoenthalpic and isoentropic processes. The low-temperature separation technology and its modifications (application of ejectors, turbo-expanders, gas-dynamic separators and vortex tubes in process diagrams). Application of thermodynamic inhibitors (methanol, MEG) for hydrate control. Promising low-temperature technological schemes for gas processing at field conditions. |
Vladimir Istomin | 3 | MA030291 | ||
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Gauge Fields and Complex Geometry (Term 3-4)
1. Self-duality equations, Bogomolny equations.
2. Relation to holomorphic bundles. 3. Relation to holomorphic bundles on twistor space. 4. Conformal symmetry and complex geometry in twistor space. 5. Elements of superfield formulation of SUSY field theories. 6. Chirality type constraints and complex geometry. 7. Some examples of superfield theories which require complex geometry. 8. BPS conditions in SUSY theories and complex geometry. 9. Elements of Hitchin's integrable systems and related complex geometry. |
Alexey Rosly |
63 per term
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MA060178 | ||
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Geometric Computer Vision
Geometry plays an extremely important role in many computer vision algorithms as certain kinds of geometric transformations (e.g., projective) form the basis of imaging, estimation, and reconstruction. This course focuses on processing the geometry of 3D scenes and shapes, as obtained from both images and depth sensory data, using a series of learnable approaches. We will cover the standard geometry processing pipeline, study the depth acquisition systems, and dive into a variety of deep learning methods defined on semi-structured and unstructured geometric datatypes. Geometric learning-based systems differ from conventional ones by needing a custom way to construct low-level building blocks such as convolutional operations, that do not naturally exist for many geometric data structures. To this end, we will consider both familiar structures such as 2D images and 3D volumetric grids, and purely geometric ones such as point sets, meshes, implicit functions, and CAD representations such as parametric models.
The course extensively leverages python programming skills focusing on numerical libraries such as numpy/scipy/pytorch, and requires basic knowledge of deep learning. Most of the software used within the course will be provided as docker images, thus knowledge of C++ or other tools should not be required. |
Alexey Artemov | 3 | MA030362 | ||
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History and Philosophy of Science
The aim of this course is to give to Skoltech students and postgraduates basic information about the main stages of the development of science from its birth in Ancient Greece through the Middle Ages and the Renaissance to Modern Times and to the great scientific revolutions of the XX century. Every man of culture especially a future scientist should know the impact of such great thinkers as Plato, Aristotle, Thomas Aquinas, John Buridan, Nicholas of Cusa, Copernicus, Galileo, Descartes, Newton, Boscovich, Darwin, Mendel, Bohr and Einstein (omitting many other brilliant names, that would be spoken about in the frames of the course) to the development of a scientific picture of the universe. Also there will be discussed the main topics and notions of the philosophy of science: demarcation between science and humanities, Popper’s theory of falsification, Kuhn’s theory of scientific revolutions, philosophical ideas of Lakatos and Feyerabend.
The course will consist of 18 3-hour lectures and 6 examination sessions (3 hour each). The students are to submit 6 written essays in English on the following themes: 1. Ancient Greek and Roman Science; 2. Medieval and Renaissance Science; 3. Scientific Revolution of the XVII century; 4. Science in the XVIII and XIX century; 5. Science in the XX-XXI centuries; 6. Philosophy of Science. For the final exam the students should prepare 10-15 minutes oral presentation on the scientific problem they are currently working upon, or on some topic connected with history of science and/or philosophy. Lectures 1-3 are devoted to the Ancient Greek and Roman Science Lectures 4-6 are devoted to Medieval and Renaissance Science Lectures 7-9 are devoted to the Scientific Revolution of the XVII century Lectures 10-12 are devoted to Science in the XVIII and XIX century Lectures 13-15 are devoted to Science in the XX-XXI centuries Lectures 16-18 are devoted to the Philosophy of Science |
Ivan Lupandin | 6 | DG060026 | ||
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Information and Coding Theory
The aim of the course is to explain basic ideas and results of information and coding theory, some of which has been used for rather long time in data science, in particular various entropy inequalities, and some emerged just very recently, for instance, usage of error-correcting codes for improvements of k-means method for clustering. The course is divided into two parts: introduction to information theory and elements of modern coding theory. In the first part, we consider the measure of information, mutual information, entropy, evaluation of channel capacity for single user and multi-user channels. In the second part, we consider foundations of coding theory such as block codes, linear codes, bounds on the code’s parameters and the most popular algebraic coding methods (Hamming, Reed-Muller, BCH and Reed-Solomon codes). Then we consider modern coding techniques, i.e. iterative decoding systems and graphical models to represent them. Iterative techniques have revolutionized the theory and practice of coding and have been applied in numerous communications standards. We discuss low-density parity-check (LDPC) codes, factor graphs and Sum-Product decoding algorithm.
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Alexey Frolov | 6 | MA060122 | ||
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Instrumental Analysis in Molecular Biology
During this course you will understand principles of main instrumental methods that are used today in molecular biology both in academia and industry. The aim of this course is to provide knowledge of modern methods for master and PhD students with minimal background in the field of molecular biology of Eukaryotes. As a result, students will understand general principles of the methods used in the biomedical research and development. By focusing on examples of the biomolecule purification and purity confirmation the idea of accurate studies will be explained. The course will provide a comprehensive summary of the major methods used nowadays in the field, except microscopy. Current trends will be reviewed, along with a discussion of methods application for common tasks. Some attention will be paid to miniaturization of analytical devices for the use as POC (point-of-care).
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Timofei Zatsepin | 6 | MA060250 | CANCELLED | |
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Introduction to Digital Agro
The agriculture and food sector is facing multiple challenges. With the global population projected to grow from 7.6 billion in 2018 to over 9.6 billion in 2050, there will be a significant increase in the demand for food. At the same time, the availability of natural resources such as fresh water and productive arable land is becoming increasingly constrained. This will require an urgent transformation of the current agrifood system.
Digital innovations and technologies may be part of the solution. The so-called ‘Fourth Industrial Revolution' (Industry 4.0) is seeing several sectors rapidly transformed by ‘disruptive’ digital technologies such as the Internet of Things, Artificial Intelligence and Computer Vision. This course will be focused on several milestone problems for the Russian Agrifood sector, like: Students will try to improve and solve this issue in real cases during the Project work |
Maria Pukalchik | 6 | MA060359 | CANCELLED | |
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Introduction to Digital Pharma
Modern pharma undergoes digital transformation. AI and other ‘digital’ technologies are actively applied in drug discovery and development to make the whole process less time consuming and more cost-effective.
The course will cover all aspects of drug design and development where digital technologies are being implemented: – Identification of drug target and its druggability assessment (genome mining), virtual high throughput screening. – Computer-aided drug design, combinatorial chemistry, exploration of chemical space. AI as a tool for chemical synthesis of new drug-like molecules. – Informatics approaches in prediction of the absorption, distribution, metabolism, elimination and toxicity (ADMET) of drug molecules. – Informatics approaches in pre-art and freedom to operate analysis. – Machine learning in clinical trials and drug repurposing. – Formulation Development – AI and Personalized Medicine. 2 invited lectures/seminars of companies developing informatics solutions for research and development of new medicines are planned. On site visit to ChemRar High-Tech Center. At the end of the course students will know the whole pipeline of drug design and development in the pharmaceutical industry. As in general this field is dynamic with changes across R&D, clinical trials, manufacturing and regulatory processes, the AI is either already applied or will be introduced in the future. The students will get hands on experience in several particular areas of drug design with specific drug target/s. The visit to the high-tech center will demonstrate how this business works in real life. |
Natalia Strushkevich | 6 | MA060418 | ||
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Introduction to Product Lifecycle Management (PLM)
Basic course for 1st year MSc students devoted to PLM as applied to product development. Lectures are devoted to an overview of current trends in industry digitalization, “digital twins” technology and modern implementation of computer-aided design, computer-aided engineering, computer-aided manufacturing, model-based systems engineering, product lifecycle management, multidisciplinary optimization, predictive and prescriptive maintenance. Practical classes are dedicated to the simulation-driven product development process in a particular case study. Students learn how to develop a high-level model of a complex system, split it into subsystems and connect it with the functional models of each subsystem and with preliminary 3D models (e.g. aerodynamics and structural analysis). Also, the optimization of the whole system plays an important role in the course. The case study is a challenging task like High-Altitude Pseudo Satellite, Truss-Braced wing aircraft or Fuel cell powered multicopter. Thus, during the course students go through all the main stages of complex system development process.
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Ighor Uzhinsky, Sergei Nikolaev |
6 | MA060148 | ||
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Introduction to Quantum Theory (Term 3-4)
One of the most striking breakthrough of the XX century is the creation of the entirely new area of physics named quantum physics. It emerged that the whole world around us obeys the laws of quantum mechanics, while the laws of classical physics that we are familiar with (such as, for example, Newton's equations) describe only macroscopic objects and can be obtained in limiting case. After that a lot of phenomena in different areas of physics found their explanation. Also quantum mechanics had a very
significant impact on the development of mathematics and mathematical physics. Today quantum mechanics is one of the keystone parts of theoretical and mathematical physics. |
Vladimir Losyakov |
63 per term
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MA060332 | ||
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IoT: Launching New Products & Startups
“IoT: Launching New Products & Startups” is an intensive 6 credits E&I course. It is designed to provide you with practical skills and experiences of translating your favorite technology into new AI/IoT/Digital products and then technology-based startups. The course uses “learning-by-doing” approach. This mean you will learn how to build new products and startups by actually building ones. In small teams you will run real-life projects performing all the key activities of early stage startups – from validating the market need and “getting out of the building” (literally!!! your team is to talk to customers, partners, users, etc.) to defining the technological solution and product, producing prototypes, designing profitable business model.
The projects themes may range from the AI/IoT application in healthcare & wellness up to education technology, industry or smart agriculture. Teams and projects will be formed around: By taking this course you will get the benefits: |
Alexey Nikolaev | 6 | E&I | MC060026 | |
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Laser Physics
The purpose of the course is to provide a solid background in the laser physics with strong emphasis on lasers for applications. The course will form a basis for the more advance courses in optics (Telecommunications by prof. Kueppers, Experimental optics by prof. Sakelaris). Lectures cover various aspects of modern laser physics including laser dynamics; ultra-fast lasers; Ti:Sapphire, fiber and semiconductor lasers; wavelength conversion and supercontinuum generation. Course will be focusing on a practical skills. During the lectures we will make a lot of problem solving and estimations. On practical seminars we will assemble our own femtosecond fiber laser and measure its emission properties.
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Yuriy Gladush | 6 | MA060143 | ||
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Machine Learning
The course is a general introduction to machine learning (ML) and its applications. It covers fundamental topics in ML and describes the most important algorithmic basis and tools. It also provides important aspects of the algorithms’ applications. The course starts with an overview of canonical ML applications and problems, learning scenarios, etc. Next, we discuss in-depth fundamental ML algorithms for classification, regression, clustering, etc., their properties, and practical applications. The last part of the course is devoted to advanced ML topics such as Gaussian processes, neural networks. Within practical sections, we show how to use the ML methods and tune their hyper-parameters. Home assignments include the application of existing algorithms to solve data analysis problems. The students are assumed to be familiar with basic concepts in linear algebra, probability, real analysis, optimization, and python programming.
On completion of the course students are expected to: |
Evgeny Burnaev | 6 | MA060018 | ||
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Master Your Thesis in English 2 (Term 7-8)
This is Spring Module of the Course.
The key to efficient professional communication is the ability to convey ideas clearly, coherently and correctly both orally and in writing. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | MF030004 | |
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Material Structure Characterization Methods
The course teaches theoretical and practical fundamentals of diffraction and electron microscopy methods applied to the analysis of the crystal structure, nano- and microstructure of materials. The course delivers basic knowledge on the theory of crystal structure analysis with various kinds of radiation, modern techniques of crystal structure determination, the analysis of the local structure of matter, defects and microstructure, theory of image formation in the electron microscope and a review on modern spectroscopic techniques with atomic resolution. The competences acquired in this course can be further used in all branches of material science dealing with crystalline matter. The course consists of lectures, seminars/practical lessons, laboratory works and exam.
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Artem Abakumov | 6 | DA060116 | ||
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MIMO Systems in Wireless Communications
This course provides an overview of modern spatial processing techniques in wireless communication systems, and shows technological aspects of massive-MIMO systems. In addition, it will be studied 3GPP standard evolution to feel requirements of high precision massive-MIMO systems from channel acquisition accuracy to algorithm robustness in case of different error influence.
We will start from basic principles of wireless communication systems; will study Shannon’s capacity, and its redefinition in terms of independent and correlated spatial channels, then move to space-time coding, spatial filters, channel acquisition techniques in 4G and 5G NR systems. Final part of the course will be denoted to specifics of radio resource management in massive-MIMO systems. After this course, the students have to be familiar with the main principles of MIMO systems in wireless communication, understand advantages of spatial domain usage, and feel the main challenges in system level and computational aspects. |
Vladimir Lyashev | 3 | MA030412 | ||
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Modern Dynamical Systems (Term 3-4)
Dynamical systems in our course will be presented mainly not as an independent branch of mathematics but as a very powerful tool that can be applied in geometry, topology, probability, analysis, number theory and physics. We consciously decided to sacrifice some classical chapters of ergodic theory and to introduce the most important dynamical notions and ideas in the geometric and topological context already intuitively familiar to our audience. As a compensation, we will show applications of dynamics to important problems in other mathematical disciplines. We hope to arrive at the end of the course to the most recent advances in dynamics and geometry and to present (at least informally) some of results of A. Avila, A. Eskin, M. Kontsevich, M. Mirzakhani, G. Margulis.
In accordance with this strategy, the course comprises several blocks closely related to each other. The first three of them (including very short introduction) are mainly mandatory. The decision, which of the topics listed below these three blocks would depend on the background and interests of the audience. |
Alexandra Skripchenko, Sergey Lando |
63 per term
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MA060257 | ||
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Molecular Neurobiology Frontier Seminar
This seminar course focuses on current advances in Molecular Neurobiology. The base of the course is current and historical publications representing a breakthrough advance in our understanding of brain function and dysfunction. The emphasis will be given to studies of the human brain and the connection between human brain functional architecture and human behavior during adulthood, development, and aging. We will also touch upon historical views and current studies of the molecular mechanisms underlying human cognitive disorders, such as autism, schizophrenia, and depression. Finally, we will consider the functional and molecular properties of the human brain, distinguishing it from the brains of other species, and discuss possible scenarios leading to the evolution of human cognition.
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Philipp Khaitovich | 3 | MA030484 | ||
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Nanooptics
Nano-optics aims at the understanding of optical phenomena on the nanometer scale, i.e. near or beyond the Abbe's diffraction limit of light. Typically, elements of nano-optics are scattered across the disciplines. Nano-optics is built on the foundation of optics, quantum optics, and spectroscopy. In the presence of an inhomogeneity in space the Rayleigh limit for the confinement of light is no longer strictly valid. In principle infinite confinement of light becomes possible, at least theoretically. The course will cover basic theoretical concepts, multiphoton microscopy, interaction of light with nanoscale systems, optical interaction between nanosystems, and resonance phenomena, namely localized surface plasmons, surface plasmon polaritons, and microresonators.
After completing the course, students will gain a general review of the field with a particular focus on modern trends in metamaterials, plasmonics and integrated photonics. In addition to that, students will be able to improve their analytical skills by solving various mathematical problems of nano-optics in a framework of the field theory. |
Vladimir Drachev | 3 | MA030153 | ||
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Neuroendocrynology
Neuroendocrinology is thе part of physiology dealing with interactions between the nervous and the endocrine system. These include interactions on molecular cellular and organism levels controlling physiological processes of the human body. This course will give advanced understanding of normal physiology and pathophysiology of the brain as an endocrine organ. The special attention will be given to hypothalamus/pituitary/adrenal axis and gonads, which maintains homeostasis, regulating reproduction, metabolism, energy utilization, control of cardiovascular system and other functions.
Modern methods in cardiac physiology, renal physiology, vascular biology. Students will learn most important techniques to study heart function, excretion and regulation of vascular resistance: ultrasound and MRI imaging, telemetry, single nephron studies, wire myography and others. |
Yuri Kotelevtsev | 3 | MA030402 | ||
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Next Generation Sequencing – Experimental Protocols and Data Analysis
Next generation sequencing is a group of methods that allow simultaneous sequencing of many thousands of DNA fragments without their physical separation by cloning. In the last 15 years it revolutionized many fields of biology: genetics, evolutionary biology, microbiology, anthropology. It have also many practical applications, especially in biomedicine. However NGS and corresponding bioinformatics methods are sometimes used in a black box regime, without understanding its logic, the area of applicability and limitations. This course will provide a comprehensive survey of both experimental aspects of NGS and of bioinformatics analysis of the data. We will also discuss the current trends in the development of sequencing technologies and their applications. The practical part of the course will offer a hands-on experience of a genomic project, starting from the experiment design, followed by preparation of DNA and RNA samples, sequencing, de novo genome assembly, annotation and analysis of differential expression.
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Maria Logacheva | 3 | MA030396 | ||
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Non-Equilibrium Processes in Energy Conversion
Classical thermodynamics is useful to describe equilibrium states, while non-equilibrium states and irreversibility characterize real physical processes. If one is interested in actual processes at work during energy conversion, a classical thermodynamic description of equilibrium states is insufficient as it yields very incomplete information on the processes. Irreversible thermodynamics accounts for the rates of physical processes, and provides relationships between "measurable quantities" such as transport coefficients. This graduate course, which constitutes the natural continuation of the course Energy Systems Physics & Engineering, provides the students with basic knowledge of out-of-equilibrium and finite-time thermodynamics, which describe irreversible processes that routinely take place in physical systems and permits a fine understanding of the processes ensuring energy conversion. Thermoelectric generators serve as the main example to illustrate in a simple fashion the out-of-equilibrium formalism, and other systems such as, e.g., solar cells are studied.
Essential notions which are taught include: Onsager’s approach to linear nonequilibrium thermodynamics; coupled transport theory; Boltzmann equation; thermal conductivity; electrical conductivity; electrochemical potential in solid-state systems; force-flux formalism and its application to thermoelectric systems; device optimization modelling accounting for dissipative coupling to heat reservoirs. The course is based both on "teaching with lecture" and "teaching with discussions" methods. In addition to home assignments, students will solve problems during tutorials and discuss their solutions. If, as for the academic year 2020-2021, the course is given in online mode, much emphasis will be put on the "teaching with discussion" to mitigate risks of superficial learning. |
Henni Ouerdane | 6 | DA060200 | ||
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Numerical Methods in Engineering and Applied Science
The course provides students with the understanding and working knowledge of fundamentals of numerical methods used for modeling and simulation of complex phenomena described by ordinary and partial differential equations. The following topics are covered: finite-difference approximation of derivatives; interpolation; integration; steady-state boundary value problems; local and global errors; stability, consistency, and convergence; matrix equations and iterative methods; initial value problems for ordinary differential equations; Runge-Kutta methods; multi-step methods; absolute stability; stiff ODE; parabolic problems; method of lines; von Neumann analysis; hyperbolic problems; upwind methods; Courant-Friedrichs-Levy condition; hyperbolic systems; dissipation and dispersion; operator splitting; introduction to spectral approximation.
The course involves hands-on experience with programming (in Matlab or Python) and solving problems on computers. Solid knowledge of undergraduate mathematics including basic understanding of the theory of ordinary and partial differential equations of physics and engineering as well as basic programming skills are required. |
Aslan Kasimov | 6 | DA060239 | ||
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Numerical Modeling
Many scientific models are formulated in terms of differential or integral equations and describe continuous quantities, such as the distribution of velocity of a fluid in a space outside an aircraft wing, distribution of stress in a solid body, price of a stock as a function of time, etc. In order to use these models in a computer simulation, the models must be discretized. The course covers a representative selection of methods of discretization of differential and integral equations. The emphasis of the course is on practical aspects of using discretization methods: intuitive understanding and formal derivation of accuracy of different methods, modelling, testing and optimizing real mechanical systems, and solving applications-informed practical problems.
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Alexander Shapeev | 6 | MA060005 | ||
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Omics Data Analysis (Term 3-4)
An avalanche of ‘omics data is coming from different sources: transcriptomics, epigenomics, lipidomics, metabolomics. A thorough analysis of such large-scale biological data sets can lead to the discovery of important biological insights on mechanisms of cell and organ functioning, and to the identification of small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions. The course will discuss general concepts of bioinformatics analysis of various types of ‘omics data and will provide examples of their successful application for solving a wide range of biological problems. The students will not only learn the best analysis practices suitable for each data type but, importantly, understand why each analysis step is necessary, what is the logic of the whole data analysis concept, which controls are essential, etc. The course will end with the task (Final Project) on the integration of different data types produced to answer the same biological question.
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Ekaterina Khrameeva |
63 per term
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MA060061 | ||
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Optical Communications (Term 3-4)
This course is the first part of a series devoted to modern optical communication systems. The aim of the course is to study the fundamental physical principles underlying modern optical communication systems. Further courses in this series will be devoted to more applied (engineering) issues in the field of optical information transmission systems. The proposed course will give the necessary knowledge from the theory of information, universal for any (not only optical) data transmission systems, in particular the Shannon theorem. Based on a unified description of stochastic processes, sources of errors in digital signal transmission will be systematically considered. Fundamentals of quantum mechanics of open systems based on density matrix will be systematically described. Using this approach, the dynamics of lasers and optical amplifiers as applied to optical information transmission systems will be analyzed. The main factors limiting the capacity and distance of the transmitted signal will be considered. In particular, the linear and nonlinear properties of the medium (mainly on the example of optical fibers) that affect the parameters of the propagating signal will be analyzed. The course will also consider modern issues of physics and technology of information transmission systems, including quantum methods to ensure the confidentiality of the transmitted signal, applications of nanophotonics and photonic integrated circuits, neural optical networks, radio photonics, as well as the role of photonics in the development of mobile networks of the fifth and sixth generations.
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Arkady Shipulin |
63 per term
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MA060157 | ||
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Optimization Methods
The course is devoted to optimization methods and optimization problems design with a special attention to those motivated by data science, engineering and industrial applications.
The course starts with a brief reminder of the foundations of convex analysis. Then we discuss zero, first and second order methods with a special focus on their efficient implementation. We distinguish between various problem classes, discuss suitable methods for every class. The problem formulation and its proper reformulation is the critical issue for an optimizer. We’ll learn about optimization models and convex relaxations. Special attention will be addressed to Linear Matrix Inequalities (LMI) that arise in optimization problem formulations. One of the home assignments is devoted to understanding the constraints of performance for different software packages. At the last part of the course we move further to advanced first order optimization methods such as proximal mirror descent and extra gradient methods and discuss how to utilize problem structure (e.g. sparsity and separability) to speed up the methods. Within engineering and practical sections, we show how to use the methods above to crack convex and non-convex problems arises in engineering, energy systems, machine learning and related fields. |
Elena Gryazina | 6 | MA060002 | ||
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Organic Materials for Energy and Optoelectronics
The course provides an introduction to physics and chemistry of organic materials as well as an overview of their applications in optoelectronics and energy conversion and storage with a focus on the development of practical skills in materials design. Students will learn about pi-conjugated semiconductors from molecules and molecular solids to polymers and materials with 2D and 3D connectivity such as graphene and metal-organic frameworks. Students will get basic skills in the design of organic field-effect transistors, solar cells, rechargeable batteries.
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Andriy Zhugayevych | 3 | MA030119 | ||
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course
describes the intended learning outcomes and how they are assessed. The main bulk of the 81 hours of the course is spent in the actual courses in which |
Dmitry Artamonov | 3 | DG030005 | ||
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Pedagogy of Higher Education (Term 3-4)
The course offers an introduction to facilitating learning in higher education for PhD students asked to act as teaching assistants. The course content focuses on high resolution constructive alignment of learning outcomes with learning activities and assessment strategies. Learning outcomes for a course are elaborated into separate activities and assignments for students. Learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. Participants in the course will therefore be required to plan and design effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments. The course therefore emphasizes communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course explores ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching assistantships or their supervisory activities to come. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson |
31.5 per term
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DG030025 | ||
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Perception in Robotics
This course will present the fundamental theory and application of perception techniques. The word perception on the context of this course will refer to the problems of Localization, Mapping and in general State Estimation. Today we are witnessing an explosion on the applications for this technology, originally developed on the robotics field, being outsourced to other domains such as self-driving cars, augmented reality, flying drones, etc. Yet there are many challenges to be solved on a wide variety of research topics. The content of the course will be mainly based on a probabilistic approach to perception problems and will examine a selected set of contemporary algorithms in depth. Topics include Bayesian filtering; Batch Estimation, Mapping, localization and simultaneous localization and Mapping (SLAM); Observation and transition functions; Read below about the course policy, final project and other details.
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Gonzalo Ferrer | 6 | MA060283 | ||
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Petrophysics and Well Log Interpretation
The course provides both theoretical knowledge in well logging application for reservoir characterization and practical skills in well logging processing and interpretation software.
During the course students will get familiar with the main well logging tools and methods, such as gamma, spontaneous potential, caliper, formation density, neutron, electrical, formation micro-imaging, sonic, magnetic resonance logging and so on. The students will study the well logs application for defining the key reservoir properties, correlation, integration with laboratory core test data. Besides lectures the course includes computer classes where students will gain applied skills in well logging interpretation software (Techlog). During the software practicums the participants will learn the main well logging data handling operations, such as creating a project for a field, import / export, displaying logs, data and variable management, depth referencing, constructing cross-plots, calculating parameters, well correlation. The proportion of lectures and software classes is approximately 50% / 50%. |
Alexei Tchistiakov | 3 | MA030289 | ||
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Physics of Colloids and Interfaces
Interface science is the basis for modern nanotechnology. Objects of the microworld are dominated by surface effects rather than gravitation and inertia. The applications of interface science are important for lab-on-chip technologies, microfluidics, biochips, tissue engineering, biophotonics, theranostics. Modern interface science is a good example of interdisciplinarity: it includes physics, chemical engineering, biology, medicine. During this course, the students gain not only theoretical knowledge but also receive practical skills related to:
– surface tension measurements; – contact angle measurements; – surface potential measurement by Kelvin probe method; – nanoparticle characterization by dynamic light scattering method for determination of size and Z-potential of nanoparticles; – synthesis of calcium carbonate cores at the micron- and submicron size and loading of calcium carbonate particles by inorganic nanoparticles and proteins; – fabrication of polymer and nanocomposite microcapsule shells by the Layer-by-Layer assembly approach. They will receive knowledge that can be used for the analysis of phenomena in the microworld from point of view of interface science. |
Dmitry Gorin | 3 | MA030310 | ||
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Power Electronics
The course provides an overview of the latest achievements in power electronics. The main purpose of the course is to analyze different circuit topologies, to understand how they work and which are their benefits and limitations. The course starts with reviewing the basics in electric circuit theory, and then, it introduces different kind of semiconductor devices such as diodes, thyristors and transistors. After this, power electronics circuits are presented: rectifiers, DC-DC converters and inverters. The course gives the tools to analyze any kind of power converters, and provides different examples related with microgrids and energy storage applications. It has three parts: lectures, home tasks and experimental activities in the lab. By the end of the course, the students should be able to analyze a power converter, to simulate it and to understand the possible applications.
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Federico Martin Ibanez | 6 | MA060198 | ||
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Practicum in Experimental Physics 1 (Term 2-3)
This course assumes mastering in certain experimental techniques in physics, including a practical work with experimental setups. The course is practically oriented, with small share of lectures. Students will have an opportunity to conduct individual research project and be familiar with unique state-of-the-art equipment.
The work can be continued in Term 4 (other 4 techniques to be chosen), or can be finished in Term 2. For Skoltech-MIPT net program, both Term 2 and Term 4 are obligatory from MIPT side, but can be substituted with other courses in frames of individual MIPT plan. |
Valery Ryazanov |
63 per term
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MA060208 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Maxim Fedorov | 3 | DD030020cd | ||
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Qualifying Exam: Engineering Systems
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Clement Fortin | 3 | DD030020es | ||
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Qualifying Exam: Life Sciences
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Konstantin Severinov | 3 | DD030020ls | ||
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Qualifying Exam: Materials Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Alexei Buchachenko | 3 | DD030020ms | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Dimitri Pissarenko | 3 | DD030020pe | ||
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Qualifying Exam: Physics
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Mikhail Skvortsov | 3 | DD030020p | ||
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Quantitative and Molecular Plant Breeding
Plant breeding is one of the most important science and technology developed by humankind.
In a context where reductions in chemical inputs are required by societal demand and national and international regulations, but where the demand for raw materials continues to increase to cope with demographic change, the genetic improvement of plants contributes to answering these major challenges, while integrating them into a sustainable development policy. The course on 'Quantitative and Molecular Plant Breeding' surveys the most suitable and efficient methods and tools available to the breeder for plant genetic improvement and new varieties development, from the evaluation of the genetic values of genotypes to classical breeding schemes dependent on the plant reproductive biology and Marker-Assisted Selection (MAS). Special attention is also given to the breeding program for disease resistance. This course is implemented on a skill-based learning and active pedagogy approach where each concept and method is illustrated during hands-on on practical real case studies tutored team mini-projects or reversed classes. It trains executives specialized in plant breeding and the creation of plant varieties. |
Cecile Ben | 3 | MA030493 | ||
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Quantum Field Theory (Term 3-4)
At present time Quantum Field Theory (QFT) is the main theoretical tool used for the description of the phenomena occurring in the microworld. Examples include interactions between elementary particles, hadron structure and so on. At the same time, QFT methods are widely used in all areas of modern theoretical physics such as condensed matter physics, statistical mechanics, turbulence theory and others. Moreover, the creation of QFT has stimulated the development of many modern areas of mathematics.
The course is aimed at the study of the basic ideas and methods of QFT, as well as the discussion of its applications in various areas of modern theoretical and mathematical physics. Topics include quantization of scalar and gauge theories, path integral approach, perturbative expansions and Feynman diagrams, (1+1) dimensional exactly soluble models and some other ideas of modern science. |
Andrei Semenov |
63 per term
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MA060316 | ||
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Quantum Integrable Systems (Term 3-4)
The course is devoted to quantum integrable systems. The history of quantum integrable systems starts from 1931 when
H.Bethe managed to construct exact eigenfunctions of the Hamiltonian of the Heisenberg spin chain with the help of a special substitution which became famous since that time (ansatz Bethe). In one or another form this method turns out to be applicable to many spin and field-theoretical integrable models. From the mathematical point of view, Bethe's method is connected to representation theory of quantum algebras (q-deformations of universal enveloping algebras and Yangians). Here is the list of topics which will be discussed in the course. – Coordinate Bethe ansatz on the example of the Heisenberg model and – Bethe ansatz in exactly solvable models of statistical mechanics – Calculation of physical quantities in integrable models in thermodynamic – Bethe equations and the Yang-Yang function, caclulation of norms of Bethe – Quantum inverse scattering method and algebraic Bethe ansatz, quantum R-matrices, – Functional Bethe ansatz and the method of Baxter's Q-operators, functional The knowledge of quantum mechanics and statistical physics for understanding of |
Anton Zabrodin |
63 per term
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MA060315 | ||
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Research Methodology for Engineering Systems
Engineering systems are necessarily of an interdisciplinary nature and specific knowledge and skills are required to successfully complete a PhD degree in the field. The objective of this course is to provide a general introduction to PhD level research and to present some common research methodologies applicable to major types of research topics in Engineering Systems. The course starts with a series of lectures of an introductory nature on the Design Research Methodology (DRM) and other experimental and simulation based approaches.
Participants will then present their preliminary research ideas, propose research objectives and proper methodology that they will present to their peers first in a pitch session; it will be followed by a more detailed research proposal and presentation. The course can be followed by a subsequent series of research seminars in various topics related to Engineering Systems as indicated by the PhD student supervisor. |
Clement Fortin | 3 | DG030102cf | ||
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Research Methodology: CDMM Research Seminar (Term 2-4)
This is the main research seminar for the Skoltech Center for Design, Manufacturing and Materials (CDMM). All MSc students either enrolled into the Master Program in Advanced Manufacturing Technologies or PhD students affiliated with CDMM should attend this seminar. The format of the seminar is weekly invited lectures from top scientists in the research fields related to Advanced Manufacturing, Digital Engineering Technologies, and Mechanics and Physics of Advanced Manufacturing will be given.
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Iskander Akhatov |
31 per term
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DG030102dm | ||
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Research Methodology: CHR Seminar
This course conducted in a form of seminar, is aimed at hands-on learning of best practices of modern research work in the academic and industrial environments. Students will learn how to formulate research objective(s), search for prior art and monitor publications, frame a research project with proper objectives, resources, timeline, and deliverables, conduct research in communication with peers and with a broader community, and, finally, present the outcomes of the research.
The seminar work constitutes the core of the course, and it will principally focus on topics related to hydrocarbon reservoir characterization, reservoir simulation, and reservoir optimization. Mathematical and numerical methods and tools relevant to this subject will be explored in depth. An equally strong emphasis will be given to geological, petrophysical, and petroleum engineering aspects of the considered models. Students will have an extensive practice in writing reports and publications, as well as in oral presentations. By attending this course, students will get a broader view of the research projects conducted in CHR, as well as a closer scientific communication with their peers and with a larger research community. |
Dimitri Pissarenko | 3 | DG030102pe | ||
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Research Methodology: Computational and Data Science and Engineering (Term 3)
A modern researcher needs to have a set of various skills in order to conduct research efficiently. In addition to high level of research skills and understanding of the research environment of one’s particular field, a researcher should be able to manage research-related business processes, be personally effective, have high level of communication and presentation skills, build effective professional relationship with colleagues and effectively manage the career development. The course covers all these topics and implies active interaction between the tutor and students during the classes. In the end of the course each student will be asked to write an essay.
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33 per term
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DG030102c | |||
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Research Methodology: Molecular Biology
We will run an NIH-type grant panel. The students will select several (no less than 3) grant applications on different areas of molecular biology/biochemistry, provide written reviews for each one following the NIH report template and will then discuss each grant in turn (a single grant will be reviewed by three reviewers). The rest of the panel members will ask questions and the panel discussion will be recorded as a summary by a scribe (different for each. In the end all grants will be scored and "funding" decisions will be taken by the panel (and compared to how the grants fared in "real life").
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Konstantin Severinov | 3 | DA030403 | ||
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Research Methodology: Space Center Seminar (Term 1B-4)
The seminar will cover current topics in the space domain: latest news, discoveries. Also planned that all PhD students and some Master students will present their research. External lecturers will be invited to focus on the main applications of space technologies: science, telecommunication, navigation and remote sensing. Aspects of space technologies will also be discussed: structures, software, attitude determination and control systems, on board computers, communication system power supply systems and others. The seminar will be offered in English.
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Anton Ivanov |
30.75 per term
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DG030102es | ||
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Research Seminar "Advanced Materials Science" (Term 2-4)
This is the main research seminar of the Skoltech Center for Electrochemical Energy Storage and Materials Science Education program featuring presentations of young researchers: MSc students, PhD students, postdocs. Every MSc and PhD student of Materials Science program should deliver at least one presentation per two years. The range of topics is broad and includes any aspects of materials science and engineering.
Please see the seminar webpage at http://crei.skoltech.ru/cee/education/wednesday-scientific-seminar/ |
Keith Stevenson |
1.50.5 per term
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DG030302i | ||
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Research Seminar "Energy Systems and Technologies" (Term 2-4)
This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8).
Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Evaluation. Filling in the Online feedback form. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. |
Elena Gryazina |
31 per term
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MA030489 | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1B-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and their theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, etc.
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Pavlo Gavrylenko |
61,5 per term
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DG060268 | ||
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Review of Materials and Devices for Nano- and Optoelectronics
This is the first part (3 credit) of the MIPT semester course "Materials and Devices for Nano- and Optoelectronics", which assumes consideration of a wide range of superconductiong devices. For complete version, take also the same course in Term 4 (second part, also 3 credits). The invited lecturers are active scientists working in corresponding areas of solid state physics and nanoelectronics. The participants are invited to analyse and present the content of original experimental paper (individual choice) related to the topic of any lecture. First, a brief presentation is discussed, and later the students present the extended improved version.
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Valery Ryazanov | 3 | MA030206 | ||
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Satellite Navigation
The GNSS data processing course is aimed to cover the modern state of technology in global navigation satellite systems applications for precise surveying, agriculture robotics, road construction and other applications. Course participants will become familiar with subjects such as:
– Navigation observables and navigation solution, – Single-, double-, triple difference techniques, – Precise point positioning, – Real-time relative positioning, – Carrier phase ambiguity resolution and integer lattice reduction, – Anomalies detection and isolation, – Basics of satellite receivers firmware design, – Geodetic surveying and practical using of GNSS receivers for accurate surveying, – Using GNSS navigation for attitude determination and motion control for wheeled robots, UAV's and other applications. |
Lev Rapoport | 6 | DA060380 | ||
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Selected Topics in Energy: Physical, Chemical and Geophysical Challenges (Term 2-4)
The course provides an introduction to the modern topics related to fundamentals of exploration of energy resources, energy generation, storage, conversion and use. It identifies the corresponding practical challenges to be addressed at the fundamental research level and familiarizes the students with the state-of-the-art approaches, methods and techniques in use in related scientific areas. The course seeks to emphasize and maintain interdisciplinary nature of the energy-related topics, in particular, combination of micro- and macroscopic approaches of geophysics, mechanics and chemistry in hydrocarbon exploration and development, relation between the physical and chemical processes of energy generation and conversion, integration of physical, chemical and mechanical approaches to perspective materials (physical and chemical synthesis, micro- and macroscopic characterization, structure-property relations, etc.) and related theoretical methodologies. These interdisciplinary links are mostly demonstrated by horizontal knowledge exchange among the students reporting and discussing practical examples from their own research field or from modern review or research publications. Topical lectures are included for further exploration of these links. The secondary aim of the course is the development of presentation skills (oral and writing), as well as scientific peer-review experience. The seminar format chosen for most activities allows students free exchange of knowledge and ideas, broader vision of their research projects and methodologies, better assessments of their own research skills and demands for further education.
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Alexei Buchachenko |
62 per term
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DG060106 | ||
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Signal Transduction in Sensory Systems
Sensory systems – vision, olfactory, taste, etc. – determine abilities of animals to detect environmental information and react immediately. The capabilities of sensory systems are striking in their sensitivity, specificity and wide adaptability. The course describes molecular and cellular mechanisms of reception of various environmental factors by animals. Signal transduction cascades are considered in detail. Besides fundamental problems, related medical issues (disorders, existing and perspective therapies) and biotechnological applications (opto-, thermo-, chemo-genetics) are discussed.
Main topics are the following: Molecular mechanisms of light sensing. Adaptation to light intensity. Color vision. Phototransduction in different taxa. Molecular mechanisms of odor sensing. Metabotropic and ionotropic odorant receptors; their broad diversity. Main types of gustation receptors. Molecular and cellular mechanisms of sound sensing. Sense of gravity and motion. Main types of temperature-sensitive channels; heat and cold sensing. Molecular mechanisms of nociception. Analgesia. Cellular and molecular mechanisms of tactile perception and proprioception. Non-visual photoreceptor proteins and their roles in controlling circadian rhythms. Phenomenology and possible mechanisms of magnetoreception. Optogenetics, thermogenetics, chemogenetics. |
Konstantin Lukyanov | 3 | MA030483 | ||
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Spacecraft and Mission Design
The main objective of the course is to introduce the concept of space system design and engineering. The course will describe the various subsystems involved in the design of a satellite. It will also describe the techniques of systems engineering that are used to obtain a coherent satellite design.
This class will focus on concept preparation in the V-diagram logic. Further results can be explored either in the Space Sector course, where commercial aspects of the mission can be considered, as well as in the PLM course, where technical details can be worked out in a systematic fashion. |
Anton Ivanov | 6 | MA060074 | ||
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Spectroscopy of Quantum Materials
The term “quantum materials” unites a broad class of very different materials demonstrating genuinely quantum behavior. Quantum materials include superconductors, strongly correlated systems, systems of massless Dirac electrons, topological materials, novel two-dimensional crystals etc. Research of quantum materials stands in the vanguard of modern photonics and condensed matter physics.
The goal of this course is to give a broad review of basic models and modern spectroscopic studies of quantum materials. The course requires basic knowledge of quantum mechanics, optics and solid state physics. In the introductory part of the course, the basic classical and quantum models of electromagnetic response are considered, and the most widely used modern methods of spectroscopy are outlined. The rest of the course describes such quantum materials as graphene and graphene-based structures, topological insulators, topological Dirac and Weyl semimetals, strongly correlated materials, two-dimensional transition metal dichalcogenides, oxide interfaces, and novel engineered quantum materials. The students will be introduced to basic models and to both conventional and ultrafast pump-probe spectroscopy studies of these materials. |
Alexey Sokolik | 3 | MA030162 | ||
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Statistical Learning Theory
This is an introductory MS/Ph.D. level course in the theory of machine learning. Our primary focus is a theoretical analysis of prediction methods, including statistical and computational aspects. There are no formal prerequisites for this class. But we will assume a significant level of mathematical maturity. This means an understanding of linear algebra, analysis, and probability. Convex optimization and machine learning will be extremely helpful but is not strictly necessary. Despite the theoretical nature of the course, students will be given a lot of practical exercises. Thus we expect knowledge of at least one programming language (Python, Julia, R, or C/C++)
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Yury Maximov | 3 | MA030417 | ||
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Structural Bioinformatics
The main goal of the Structural bioinformatics course is to introduce students to the main features of protein structures and to the explanations of the observed features.
The Structural bioinformatics course covers the following topics: In a more theoretical part of the course, we will overview (i) protein primary, secondary and tertiary structures, (ii) interactions stabilizing protein native structure (discussing the role of energy and entropy), (iii) statistical patterns observed in protein structures, (iv) protein thermodynamics, (v) protein kinetics, (vi) protein folding problem, and others. We will also discuss the design and the results of the selected experiments in the field. In a more practical part of the course, we will (i) visualize protein three-dimensional structures, (ii) align protein sequences and structures, (iii) predict three-dimensional protein structure from sequence, (iv) predict protein cellular localization and transmembrane topology, (v) predict the impact of a mutation on protein stability, and others. |
Dmitry Ivankov | 6 | MA060375 | ||
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Structure and Properties of Materials
This course is an introductory subject in the field of materials science and crystallography. The goal is to introduce students to basic concepts of structure-property relations for materials at the microscopic level.
Independent student work on discipline includes preparation for lectures, seminars, labs and other learning activities, as well as the implementation of individual tasks / independent works / projects and others. Educational and methodical support of Independent student work presented by topics of all kinds of tasks and guidelines for their implementation. |
Artem Oganov | 6 | MA060075 | ||
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Teachers Toolkit for Higher Education
The course helps TAs and PhD students to acquaint and get experience in using modern pedagogical approaches and tools. The course interactively leads through 3 teacher fundamentals – outcomes, educational events and evaluation. Participants will take a tour through paradigms of competence-based education (CBE), outcome-based education (OBE), constructive alignment, taxonomies, Kolb Cycle, Gibbs Circle, education strategies etc. All those basic approaches will be integrated by practical exercises with the outcome and curriculum design frame.
The course gives an opportunity to try two big groups of techniques – interaction-based tech (incl. group work, facilitation, fishbowl) and problem-based techs (incl. case-study, problematization, positional learning method). The course also covers evaluation and feedback as a part of effective learning. One of the special focuses of the course is tools for online learning. Modules of the course are supplemented by interactive exercises to engage participants in discussion and re-thinking on their experience concepts and technologies. Participants will design their own curriculum as the final project. |
Igor Remorenko, Kirill Barannikov, Igor Shyian, Kristina Roppelt, Alexander Vaniev |
3 | DG030039 | CANCELLED | |
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Technology Planning and Roadmapping: Foundation
Technology Planning and Roadmapping (TPR) is a key corporate function that companies put in place to understand, manage, and define technology strategy. The main goals of TPR are:
1) to provide understanding on current technology investments in the company (portfolio management); 2) to identify technology investment options for future products and services (landscaping); 3) to benchmark the company’s technology strategy against market competition and accounting for global technology trends (benchmarking); 4) to valuate future financial benefits, risks, and technical feasibility of envisioned technology investments (valuating); 5) to prioritize technology investments by analyzing potential future scenarios while accounting for corporate strategic drivers (prioritizing); and ultimately 6) to formulate recommendations for research and development (R&D) investments based on the definition of a rigorous technology strategy (planning). Technology Planning and Roadmapping: Foundations (TPR:F) covers the theoretical fundamentals of technology planning and roadmapping, including fundamental concepts, an overview of the most common tools and processes used by practitioners in the field, and application examples from companies in different sectors. In short, TPR:F is about building the intellectual foundations that will allow students to collaboratively build a TPR system for an industrial organization. The main deliverable of TPR:F is the development of a technology roadmap for a student startup or reverse-engineering of the roadmap of a company of interest to the student. |
Alessandro Golkar | 3 | E&I | DC030017 | |
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Tensor Decompositions and Tensor Networks in Artificial Intelligence
This course covers major topics in tensor decompositions and tensor networks for modern applications in Machine learning (ML), Signal Processing (SP), Deep Neural Networks (DNN), Multiviews, and Data Fusion.
The goal of the course is to provide students with a background in mathematics, especially in linear and multilinear algebra, statistics, useful computational tools. The emphasis of this course is on "learning by experimenting and programming". Students will learn various tensor decomposition models and state-of-the-art algorithms for each model. Students will study also challenging problems, e.g., degeneracy, stability, model selection, and know how to deal with them in practice, make the models scalable for big data, solve the complex tensor networks. Lectures will be strongly coupled with a number of hands-on sessions. Students will have the opportunity to understand how tensor networks are applied to Machine Learning problems, e.g., for Deep Learning and NLP, Signal Processing applications. Topics: Background: Basic tensor decomposition methods: Practical applications: |
Anh-Huy Phan, Andrzej Cichocki |
6 | MA060468 | MOVED FROM T5 | |
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Thermal-Fluid Science
Thermal-Fluid Science course designed for Skoltech students who need exposure to key concepts in the thermal-fluid science to apply them in research programs of various Skoltech CREIs. The course includes some chapters of Thermodynamics, Fluid Mechanics, and Heat and Mass Transfer.
Course Goals: 1. This course will provide the students with an understanding of the first and second laws of thermodynamics and their application to engineering systems. At the end of this course, students will be able to solve typical problems involving application of the first and second laws of thermodynamics to pure substances. This will include understanding and using the property tables. 2. This course will provide the students with an understanding of the basic principles of fluid dynamics and their application to engineering systems. At the end of this course, students will be able to solve problems involving fluid dynamics typical for a mechanical engineer. 3. This course will provide the students with an understanding of the basic principles of heat and mass transfer and their application to engineering systems. At the end of this course, students will be able to solve problems involving conduction, convection, radiation heat transfer as well as heat exchanger design. This will include understanding and using the property tables. 4. The students will also be required to work as part of a team on an open-ended research/design project. This project will involve the application of concepts learned in the course. |
Iskander Akhatov | 6 | MA060491 | ||
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory 6 credits component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 | ||
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Vertex Operator Algebras (Term 3-4)
Infinite-dimensional Lie algebras (such as Virasoro algebra or affine Kac-Moody
algebras) turn out to be very important in various areas of modern mathematics and mathematical physics. In particular, they are very useful in the description of some field theories. In this context one arranges infinite number of the Lie algebra elements into a single object called field. This idea generalizes to the general theory of vertex operator algebras. VOAs capture the main properties of the infinite diemensional Lie algebras and have rich additional structure. Vertex operator algebras proved to be very useful in many situations; the classical example is the KP integrable hierarchy. They are also extensively used in modern algebraic geometry. Our goal is to give an introduction to the theory of vertex operator algebras from the modern mathematical point of view. We describe the main definitions, constructions and applications of the theory. The course is aimed at PhD students and master students. |
Evgeny Feygin |
63 per term
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DA060259 |
Term 4
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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3D Bioprinting: Processes, Materials, and Applications
Three-dimensional (3D) printing represents the direct fabrication of parts layer-by-layer, guided by digital information from a computer-aided design file without any part-specific tooling. Additive manufacturing technology offers significant advantages for biomedical devices and tissue engineering due to its ability to manufacture low-volume or one-of-a-kind parts on-demand based on patient-specific needs, at no additional cost for different designs that can vary from patient to patient, while also offering flexibility in the starting materials. Bioprinting requires a broad range of expertise from different major disciplines, namely, biology (e.g. tissue and cell behaviors), mechanical engineering (e.g. additive manufacturing, machine design and control and CAD/CAM) and materials science (e.g. biomaterials, fluid behavior).
The main goal of 3D bioprinting course is thus written to bridge the gaps between the abovementioned three disciplines, providing not only the fundamentals, but practice knowledge. The course starts with the introduction of tissue engineering (TE) and the scaffold-based TE approaches. Big part of course will be devoted to main processes of 3D biofabrication. We will describe the three key stages in 3D bioprinting, which are pre-processing (biomaterials and cell source), processing (the 3D bioprinting systems and processes) and post-processing (cell culture). The application areas of bioprinting, including tissue engineering and regenerative medicine, clinics and transplantation, pharmaceutics, and cancer research, the future trends in bioprinting that will revolutionize the organ transplantation technologies in the next decades will be discussed. During laboratory class, students will get acquainted with the bio additive technologies on various bioprinting installations and biomaterial part mechanical testing. |
Igor Shishkovsky | 3 | MA030354 | MOVED FROM T6 | |
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Academic Communication: Preparatory English for Phd Exam (Term 3-4)
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. The course is aligned with the NATURE MASTERCLASS available to Skoltech researchers, so you will be able to benefit from professional recommendations of the Nature experts regarding the structure and contents of a publication, and constructive feedback from your Instructor on the language of your materials. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities, to be performed in class and at home, individually and in teams. By the end of the course, successful participants will – know the rules and conventions of research paper writing, including structure, style, grammar and vocabulary; – improve their academic communication skills, such as active listening, spontaneous and rehearsed speaking/ presentation, reading and writing within a given academic genre; – have experience in writing, editing, peer-reviewing and presenting research results. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | DF030029 | |
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Academic Writing Essentials (Term 3-4)
With the growing demands for every scientist to publish and not to perish, the quality of academic writing is of utmost importance. Successful writing presupposes the skills to communicate ideas, theories and findings as efficiently and clearly as possible. The way ideas are communicated is different in Russian and English Academic discourse. The course will discuss successful strategies and typical tactics to communicate science in English.
The aim of the course is to help the students plan the written work, understand its major parts, use the rhetorical devices, and master the linguistic repertoire appropriate in biological academic discourse. The integrative approach unites the top-down and the bottom-up ones. The general logic as well as the minute linguistic devices for presenting, advancing, and reformulating the argumentation will be given. The course teaches how to write, revise and edit your own work in a lingua franca of modern science. The course will familiarize the students with major problems the Russian authors have in the English formal writing as well as the ways to overcome them. Extensive writing, listening to lectures, self- and peer- editing and getting feedback from the lecturer will provide grounds for future autonomous writing in the discipline of biology ( including papers and a Master Thesis). |
Anastasiia Sharapkova |
31.5 per term
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Extra | MF030002l | |
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Academic Writing Essentials (Term 3-4)
Academic writing skills are necessary for effective research, innovation, and educational activities in a multinational setting. The aim of the course is to provide guidelines and strategies for writing academic texts, focusing on relevant aspects of grammar, vocabulary, and style. The course includes analysis and practice of various forms of scientific and technical writing, and builds writing skills from sentences to paragraph structure, from summary to abstract, and lays the foundations for writing scientific papers and Master Thesis.
Modern science is, for most purposes, a collective collaborative effort, so the course is designed to promote individual and group responsibility by providing mutually related and time-dependent tasks, such as peer review. The course is writing-intensive with ample opportunity to practice editing and peer-reviewing. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | MF030002 | |
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Advanced Manufacturing of Composite Materials
This course is developed to give students a broad background and hands on experience in manufacturing of advanced composite materials. Both materials and manufacturing methods are discussed. A brief introduction to advanced composite materials and processes is presented. The course is focused on the innovative non-autoclave technologies of thermosetting resin based/fiber reinforced advanced composites. Manufacturing is covered in terms of the major steps required to fabricate laminated composite parts. These will be described and discussed in details and worked out experimentally through conducting a set of lab projects. The following technologies and methods will be covered: Vacuum Infusion, Press Molding, Pultrusion, Filament Winding, and Mechanical Testing. Typical problems of materials, tooling, cure, and technological defects will be discussed.
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Alexander Safonov | 6 | MA060298 | ||
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Advanced Materials Modeling
The course builds on introductory Computational Chemistry and Materials Modeling course to provide in-depth understanding and advanced-level use of commonly employed modeling methods, as well as teach state of the art methods tailored for modeling of specific classes of materials and physical processes relevant to multiple major industries in the future, including nanotechnology and energy. The emphasis is on deep understanding and practical use of techniques, algorithms and programs to bridge theory and applications, from the discovery of materials to their use in real-world technologies. Personalized advisory of several experts in different areas of computational materials science will allow students to accomplish challenging projects related to their PhD/MSc theses or other research in materials science.
At the end of the course, students will learn advantages and limitations of various approximations in electronic-structure modelling, both within the framework of density-functional theory and beyond (including many-body perturbation theory). Methods for describing effects of external factors such as temperature, pressure, and doping on properties of materials will be also discussed. Students will also learn modern methods of artificial intelligence for materials design. During practical lessons students will learn to perform calculations of atomic and electronic structure of materials and evaluate reliability of obtained results. Students will also have an opportunity to apply in practice molecular dynamics based methods, as well as machine learning methods. Students will master electronic-structure packages Abinit and FHI-aims, as well as other programs, including advanced molecular dynamics and machine learning algorithms. |
Sergey Levchenko | 6 | DA060341 | ||
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Advanced PLM Techniques: Digital Design and Optimization
This course is dedicated to the end-to-end design methodology, based on the PLM approach. During the course students will develop small unmanned aerial vehicle with deployable wings.
The design includes: concept development, conceptual design, systems engineering, 3D physical simulation (CFD and FEM), parametric and topology optimization, final solid design. Educational process is focused on teamwork in this course. Siemens Teamcenter PLM platform is used as to provide interaction within students workgroup. The course provides students with a theoretical and practical basis for implementing projects devoted to the design of complex technical systems, such as unmanned aerial vehicles. |
Ighor Uzhinsky | 6 | MA060252 | ||
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Advanced Topics in Bioinformatics and Genomics 2
This part of Advanced topics in bioinformatics and genomics is dedicated to next generation sequencing (NGS) methods, their principles, limitations, and data analysis. This course will provide a comprehensive survey of experimental aspects of NGS, the current trends in the development of sequencing technologies and their applications. In particular, the following topics will be covered: de novo assembly, differential gene expression and splicing analyses, footprinting assays, chromatin architecture, and epigenetics.
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Dmitri Pervouchine | 3 | DA030439 | ||
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Advanced Topics in Cell and Molecular Biology 2
The course showcases Skoltech Life Sciences faculty whose research falls under wet biology. Each faculty will give four lectures presenting their own work and, more broadly, the state-of-the-art in his/her area of expertise. The lectures will be followed by Q&A sessions and are expected to foster collaborations between different faculty and grad students and provide the students with a broad view of research conducted by the faculty. This course (or the sister course on advanced topics in bioinformatics) is required for PhD students but may be taken by Masters students looking for a laboratory to perform a thesis project. To fulfil curricular requirements this three-credit module may be combined with another advanced topics in cell and molecular biology module or with a bioinformatics module.
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Konstantin Severinov | 3 | DA030436 | ||
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Biomaterials and Nanomedicine
This module will define and describe biomaterials in general term with particular attention to modern concepts of nanomedicine. It will include basics in properties of Biomaterials Surfaces considering their Physics and Chemistry, Methods of surface modification and characterization, approaches to fabricate Biocompatible surface. This will follow aspects of Protein – surface and Cell-Surface interaction. Biomaterials part concludes with fabrication and use of Scaffolds, implants, eluting stents and gels for tissue engineering
Nanomedicine part includes overview of Micro- & Nanoparticles application as biomaterials, concept of Drug Delivery systems, Controlled and Triggered release Course include considering recent original and review paper by students and lab work on formation of biomaterials of certain sort and their characterisation |
Gleb Sukhorukov | 3 | MA030405 | ||
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Biomedical Application of Photonics
The overview of the current state of photonics application in biology and medicine will be presented including optical properties of a cell, biological tissue, body (absorption, reflection, scattering, fluorescence). Now the photonic tools are used for imaging, diagnostics, manipulation, therapy, and surgery at three different levels – cellular, tissue, and body, therefore the course aims to teach students to understand basic principles of the current biomedical applications of photonics tools. Every level is required to apply a different approach, for example for cellular-level imaging, manipulation, Confocal LS Microscopy (including the technology of quotative analysis as FRAP, photoconversion, FLIP, FLAP, FRET, FLIM, FCS, FCCS), darkfield microscopy; optical tweezers approach, laser cell poration; for diagnostics – Raman microscopy, CARS, in vitro and in vivo fluorescent flow cytometry, in vivo flow photoacoustic (PA) cytometry; and for therapy – laser-induced necrosis and apoptosis; in vivo flow PA setup for theranostics are used. Tissue level requires imaging, multiphoton microscopy, SHG and THG microscopy, OCT, raster-scan optoacoustic mesoscopy (RSOM); for manipulation – laser 3D printing, laser skin perforation. Body level includes for imaging – OCT, MRI, CT, MRI, fluorescence and optoacoustic imaging, US, PET; for diagnostics – different types of in vivo sensors including implantable medical devices, smart tattoo; for therapy – photodynamic and photothermal therapy; for surgery – photonic approach guided surgery including endoscopy, high speed surgery with the highest resolution. Topics also include description of different types of contrast and optical clearing agents. The course will also offer a practice in operation of imaging systems on the cell, tissue and body level such as fluorescent microscopy, RSOM, fluorescence imaging. Students will have experience related to application the most appropriate photonic tools for their own research projects.
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Dmitry Gorin | 6 | MA060158 | ||
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Biomedical Imaging and Analytics
This course is designed for Machine Learning and Data-Science students who would like to concentrate their research on the analysis of biomedical images. This cohort of specialists – especially early on in their careers – is known for dismissing both the physical mechanisms of image formation and the very biological rationale behind a given imaging modality. In this course, we will attempt to reunite the three disciplines in order to help students develop systematic analytical expertise and biological intuition.
The course is also aligned with the curriculum of the centers of life sciences and photonics and should be used for enriching those offerings with modern machine learning and image analytics skills. Students will learn what forms the backbone of biomedical imaging, drawing from the mathematical, physical, chemical, and biological sciences, including the subjects of: • Light microscopy (live cell imaging, deconvolution and superresolution microscopy, 3D microscopy, Optical Coherence Tomography); • Medical imaging (X-ray, Computed Tomography, Magnetic Resonance Imaging, Ultrasound, Positron Emission Tomography); • Image analytics (filtering and signal processing, machine learning and artificial neural networks, computer vision, image-based biological and physiological modeling). The course is offered primarily for CDISE students who are assumed to know the basics of image processing and machine learning. In addition to the lectures, there will be 6 adapted seminars for those students who encounter the technical aspects of this course for the first time (e.g., Life Sciences students). The technical sessions will be shuffled with invited seminars by doctors/biologists with whom there is an ongoing collaboration. |
Dmitry Dylov | 6 | MA060305 | ||
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Carbonate Reservoir Geology and 3D Modeling
The course provides both theoretical knowledge in carbonate reservoir geology and practical skills in 3D geomodeling software.
During the course students will get familiar with the most important essentials of carbonate reservoir geology and will gain a consistent theoretical knowledge in applied subjects required for scientifically-minded geological modeling. In particular, the participants will learn depositional systems and associated lithofacies, reservoir structural geology, application of sequence stratigraphic approach to reservoir modeling, reservoir petrophysical properties applied for reserves calculation and so on. Besides lectures, the course includes computer classes where students will gain applied skills in 3D geomodeling software (Petrel). During the software practicums the participants will learn the main geomodeling operations, such as creating a project for a field; import / export, displaying and handling different types of geo-data; construction of a reservoir stratigraphic and structural frameworks; facies modelling; petrophysical modeling and reserves calculation; project presentation and delivering the geomodel to an end-user. The proportion of lectures and software classes is approximately 50% / 50%. This course is the final course in the Reservoir Geology and Property Evaluation triad that consist of the courses in Reservoir Rock Characterization (and laboratory core analyses), Petrophysics and Well Log Interpretation; and Reservoir Geology and 3D Modeling. Upon completion of these courses, the students will get a comprehensive knowledge and practical skills required from a modern reservoir geologist operating hydrocarbon and geothermal reservoirs as well as subsurface energy storages. |
Alexei Tchistiakov | 3 | MA030467 | ||
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Cell Biology Lab Course
Lab course in Cell biology provides students an opportunity to explore how the techniques of molecular and cell biology may be used to understand cell function. Laboratory practice in cell biology will provide the experience in genetic manipulations with cell lines, immunostaining and fluorescence or confocal microscopy analysis. The main aspects for FACS-analysis and cell sorting will be introduced. The approaches for gene expression analysis by RT-qPCR, Western blot and differential proteome analysis will be used to understand the influence of genetic manipulation to the cell function. The introduction in powerful approach to understand the protein interaction such as SPR optical biosensor will be provided.
The introduction in high-throughput screening of biologically active compounds will be provided. The course will provide students with a hands-on understanding of modern methods of cellular manipulation and understanding the mechanism of cell functioning. |
Olga Dontsova | 6 | MA060134 | ||
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Communication Technologies for IoT
The course "Communication Technologies for IoT" prepares students for the applying modern telecommunication technologies, both wired and wireless, in the Internet of Things area. The course combines lectures and labs related to hardware, transmission techniques, the medium-access control layer, networking, applications and standards for the IoT communication technologies. All technologies are considered with use-case based approach that shows their practical application in real industrial and research scenarios.
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Dmitry Lakontsev, Kirill Andreev |
3 | MA030234 | ||
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Computational Fluid Dynamics
Fluid flows are ubiquitous in engineering. Fluid mechanics provides the theoretical foundation to a broad spectrum of engineering applications that range from tiny laboratory-on-a-chip devices to the largest thermal and hydroelectric power plants. The rich dynamics of fluid motion leads to numerous effects that engineers may wish to exploit or suppress. Mathematical description of fluid flows most commonly involves non-linear partial differential equations that make analytical solution impossible or impractical. Therefore, approximate solution using numerical methods has been widely implemented since the advent of digital computers. Nowadays, the Computational Fluid Dynamics (CFD) is a well-established discipline. CFD is widely used in science and engineering alike. It accelerates optimal design and offers important insights in the flow dynamics.
The course will introduce the students to important theoretical and practical aspects of the CFD. It will explain how to describe the fluid flow by partial differential equations with suitable initial and boundary conditions, and how to transform those equations into computer algorithms. A brief overview of general-purpose numerical methods will be provided, with comments on their relevance to the CFD. Then, specialized methods for different types of flows will be introduced. This will be followed by a brief discussion of the advanced topics of fluid-structure interaction, turbulence modelling and high-performance computing. Computer practice classes will allow the students to acquire basic skills of programming simple methods from scratch, get acquainted with existing CFD software packages, develop intuition for distinguishing physical effects from numerical artifacts, and learn to use the CFD wisely. |
Dmitry Kolomenskiy | 6 | MA060450 | ||
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Computational Materials Science Seminar (Term 3-4)
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov |
1.50.75 per term
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MA030430i | ||
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Computational Nonlinear Dynamics
Real systems are nonlinear. Nevertheless, we attempt linearizing whenever possible because linear theory is well established and rather straightforward. However, when we linearize, we also lose essential information. Thus, future engineers should be capable of recognizing nonlinear mechanisms and understand their possible significance. Considering nonlinearity during the design of a new system can permit to wider the design space. Nonlinearity can be used to prevent high vibration level and risk of high cycle fatigue.
The course will introduce the students to important theoretical and practical aspects of Nonlinear Dynamics of mechanical systems. It will explain how to identify nonlinear response and the source of nonlinearities. Concepts of stability and bifurcation will be introduced. The state-of-the-art numerical techniques for nonlinear vibration analysis will be introduced. The students will have the opportunity to implement the numerical technique on different mechanical system. They will use finite element software to perform vibration analysis of real industrial components. |
Loic Salles | 6 | MA060451 | ||
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Data Analysis for Space Weather
The course introduces students to Solar-Terrestrial physics, Space Weather and practically useful approaches of data analysis for study, forecasting, and mitigation of space weather effects. The course provides an overview of Sun-Earth connections, starting from the interior of the Sun and ending in the Earth's magnetosphere. To gain insight into this field, we focus on such topics as: solar interior and solar structure, solar atmosphere, solar wind, solar flares and coronal mass ejections, as well as associated geomagnetic storms and polar auroras. These phenomena drive Space Weather with the implications for space-borne and ground-based technological systems (satellites, human spaceflight, airlines, power systems and pipelines). We also examine the space weather effects on technology and human health, hazard assessment, mitigation and forecasting, space environment data, scientific and service products.
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Tatiana Podladchikova | 6 | DA060309 | ||
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Deep Learning
The course is about Deep Learning, i.e. a new generation of neural network-based methods that have dramatically improved the performance of AI systems in such domains as computer vision, speech recognition, natural language analysis, reinforcement learning, bioinformatics. The course covers the basics of supervised and unsupervised deep learning. It also covers the details of the two most successful classes of models, namely convolutional networks and recurrent networks. In terms of application, the class emphasizes computer vision and natural language analysis tasks. The course involves a significant practical component with a large number of practical assignments.
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Victor Lempitsky | 6 | DA060057 | ||
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Design of Chemical Sensors: from Fundamentals to Applications
The course is devoted to the development of chemical sensors and the assessment of their analytical figures of merit mainly from a viewpoint of material science with an emphasis on functional nanomaterials.
The sensors are about signal transduction of a chemical interaction to a physical signal which can be measured or assessed by a human. As most processes, which are intended to be a basis for sensor development, occur on the surface, they involve physical and chemical phenomena. Thus, it is important to understand such a physico-chemical system as a whole that leads to selective, sensitive recognition of specific interaction. In our course, we will examine and go from the basics of surface physics and chemistry to the real applications of materials and nanomaterials in chemical sensors and analytical systems. We will study the operation principle of the most widely applied sensors like resistive, electrochemical, acoustic, mass, biosensors, and optical ones. The synthesis of sensitive materials and the design of chemical sensors will also be considered. We will finally touch on the multisensor system and data analysis, including the machine learning protocols. |
Fedor Fedorov | 3 | MA030446 | ||
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Developing Products and Services Through Design Thinking
The complexity and uncertainty of the world of business require a set of skills that combine analytical approaches with creative ones. The challenges are more often unstable, unpredictable, and complex. To be competitive in this environment, specialists should be able to combine analytical and creative approaches.
During the course, we will follow the Design Thinking approach to tackle an innovation challenge. The course is set-up like a workshop, where teams work on their challenge on a weekly basis receiving lectures that introduce practical methods that can soon be put into practice. Design Thinking is a process that iteratively seeks to understand user needs, challenge well-established assumptions, and redefine problems. |
Alexander Chekanov | 3 | E&I | MC030022 | |
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Digital Technologies in Electrical Grids
This course is focused on how modern digital technologies are changing the business processes in power grid companies over the world now. Particular attention is paid to the successful and unsuccessful experience in the implementation of technologies such as AI, IOT, BD, communication technologies, new manufacturing technology, blockchain in power grid companies, as well as to analysis of the digital technologies impact on their business processes. The course provides an overview of the largest power grid companies: how they are arranged their organizational structure and business processes; what the challenges are that they face today; which digital technologies they have already implemented, which ones are being tested, and what technologies they consider promising.
Students will learn how to analyze strategies in the field of digitalization of power grid companies and provide an expert assessment of the chosen strategy, taking into account the technological maturity of the company, as well as other internal and external factors. |
Dmitry Titov | 3 | MA030476 | ||
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Dynamics and Control in Robotics
Robots are nowaydas perhaps the best physical media for installation of AI systems. To be on par with the trend for AI-driven automation through robotics, it is necessary to be sound in modeling and control of robots.
The current course will teach you the fundamentals of robotic systems. These include kinematics, dynamics, control and elements of formal system analysis. Completion of the course will enable a groundwork for synthesis and application of sophisticated modern control algorithms, such as reinforcement learning, in regard to robots. In the dynamics part, the course spans such concepts as generalized coordinates, forces, virtual displacements, equations of motion etc. Forward, inverse dynamic problems are addressed. Several formalisms for complex multi-body systems, such as Euler-Lagrange, Hamilton, Newton-Euler are studied. All the content is rich of concrete examples. Properties of equations of motion are thoroughly analyzed. The second part of the course is dedicated to robot control, starting with basic path planning via simple controllers, such as PID, and continuing to such advanced techniques as predictive, backstepping, adaptive, sliding-mode controllers etc. Finally, methods of formal analysis for stability and safety are addressed. |
Pavel Osinenko | 3 | MA030457 | ||
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Electrochemistry: Fundamentals to Applications
This course covers fundamental concepts of electrochemistry: oxidation and reduction processes, types of conductors, electrolytes, classification of electrodes and electrode reactions, Faraday’s Laws, and electroanalytical methods. In addition, some applied aspects of electrochemistry will be covered including industrial electrolytic processes, electrodeposition, and electrochemical power sources (batteries and fuel cells).
The prerequisites are: undergraduate math, chemistry, and physics. |
Keith Stevenson, Victoria Nikitina |
6 | MA060127 | ||
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Embedded Systems and Intelligent Sensors
This module will give a wide-ranging introduction to sensors and embedded systems in the scope of Internet of Things (IoT) paradigm. The module aims at providing full support to the non-engineering students with a series of carefully constructed concepts and exercises. It starts with setting the whole picture of IoT and its requirements for sensors and embedded systems. Then it introduces basic principles and simple projects, and moves towards more advanced IoT system design. Finally, the module will make overview of targeted applications including Smart-X, Oil & Gas industry, wearables and medical applications.
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Andrey Somov | 6 | MA060474 | ||
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Engineering Physics
Engineering Physics course have been designed to help students gain an understanding of the key elements intrinsic to the subject. Engineering Physics deals with the physics of substances that are of practical utility. This course focuses on the changes in properties of materials arising from the distribution of electrons in metals, semiconductors and insulators. It covers topics on crystallography, free electron theory of metals, principles of quantum mechanics, superconductivity, properties of dielectrics and magnetic materials, lasers, fiber optics, holography, acoustics of buildings and acoustic quieting, optics, non-destructive testing using ultrasonics, nuclear physics, and electromagnetic waves. A list of important formulae, solved problems, and review questions will be considered for the recitations.
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Vladimir Drachev | 3 | MA030434 | ||
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English
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research and follows the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, last of three weeks and take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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Experimental Optics
Course description: The experimental optics course focuses on providing students with the basic practical skills required to kick-start a successful photonics career. The course is centred around four experimental projects that are relevant to different areas of photonics: single photon counting (demonstrating the particle nature of light), holography (demonstrating the wave nature of light), polarization, a very important property of light and introduction to fundamental measurement techniques. The course includes a general introduction to lasers and optoelectronics and regular lectures which are relevant to the specific topics of the experiment. The students will work semi-independently in small group of 2-3 students with possible supervision from a teaching assistant.
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Sakellaris Mailis | 6 | MA060336 | ||
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Fundamentals in Methodology of Scientific Research
This course teaches and train basic methodological skills common for every Materials Science and Engineering MSc and PhD student but not specific to any particular research direction: how to organize your research, report and disseminate your results, prepare and submit your manuscript to a journal, write a proposal, patent your discovery, get industrial project, launch startup, refine your CV, etc. The course consist of independent blocks, each block can be passed without attending classes if you already have required skills.
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Andriy Zhugayevych | 3 | MA030342 | ||
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Fundamentals of Post-Quantum Cryptography
In this course, we will cover modern techniques of post-quantum cryptography that become more and more popular due to recent advances in quantum-computers and quantum algorithms for solving classical mathematical problems forming the basis of current cryptography-techniques. For example, the problem of big number factorization on what RSA is based can be solved by the Shor algorithm. The course will be divided into three main parts:
1. Classical cryptography |
Grigory Kabatyansky | 3 | MA030408 | ||
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Fundamentals of Remote Sensing
This course introduces students to the first principles and methods of the observation of Earth surface, monitoring of Earth atmosphere and detection of different kind of radiation coming from Space. The course will cover wide range of the satellites-, aircraft-, rockets- and balloon- based techniques designed for environmental monitoring, meteorology, map making etc. Goals of the course include: a comprehensive knowledge of the principles and approaches to the creation and operation of remote sensing systems; acquisition of analysis skills of modern ERS programs; practical application of acquired knowledge and
skills for SWOT-analysis of complex information systems. Course will also include a module on geomatics, i.e. platforms, sensors and methodologies related to the collection, processing, analysis and interpretation of (2D/3D) data related to Earth's surface. This includes platforms like satellite or drones, sensors like LiDAR or airborne cameras and techniques like photogrammetry, laser scanning, geodesy, topography, etc. Major learning outcomes include operational principles and design of different sensors used in remote sensing of the Earth, practical skills to design an experiment in remote sensing with applications to a practical business need. |
Anton Ivanov | 6 | MA060186 | ||
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Gauge Fields and Complex Geometry (Term 3-4)
1. Self-duality equations, Bogomolny equations.
2. Relation to holomorphic bundles. 3. Relation to holomorphic bundles on twistor space. 4. Conformal symmetry and complex geometry in twistor space. 5. Elements of superfield formulation of SUSY field theories. 6. Chirality type constraints and complex geometry. 7. Some examples of superfield theories which require complex geometry. 8. BPS conditions in SUSY theories and complex geometry. 9. Elements of Hitchin's integrable systems and related complex geometry. |
Alexey Rosly |
63 per term
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MA060178 | ||
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Geometric Modeling
Classification, principles and techniques of digital modelling of point sets are presented for points, curves, surfaces and solids. Specifically these include methods of modelling of point clouds, depth fields, parametric curves and surfaces, implicit surfaces and solids. Solid modelling includes such representations as Constructive Solid Geometry (CSG), Boundary Representation (BRep) with polygonal meshes and parametric surfaces, sweeping, spatial occupancy enumeration, and Function Representation (FRep).
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Alexander Pasko | 6 | MA060297 | ||
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Geometrical Methods of Machine Learning
Many machine learning problems are fundamentally geometric in nature. The general goal of machine learning is to extract previously unknown information from data, which is reflected in the structure (underlying geometry) of the data. Thus, understanding the shape of the high-dimensional data plays an important role in modern learning theory and data analytics.
Real-world data obtained from natural sources occupy usually only a very small part of the ‘observation’ space and concentrate non-uniformly along lower dimensional structures, and geometrical methods allow discovering the shape of these structures from given data. A large part of the course addresses to most popular geometrical model of high-dimensional data called manifold model and introduces modern manifold learning methods. The course includes also Topological Data Analysis that is an emerging trend in exploratory data analysis and data mining and provide a general framework (a set of topological and geometric tools) to analyze high-dimensional, incomplete and noisy data. Necessary short information on differential geometry and topology will be given in the course. The course will provide examples of the application of geometric and topological methods of data analysis to various applied problems. The course is useful for MSc and PhD students interested in recent geometrical methods, lying at the interface between Mathematics and Machine learning. At the end of the course, students will know the basic ideas of a geometric approach to data analysis, possess modern geometric and topological methods of data analysis and be able to apply them to solve basic machine learning problems, such as classification, regression, dimensionality reduction, data presentation and visualization, clustering and others. This knowledge and skills allows them to participate in real-life projects to solve complex applied problems of data analysis. |
Alexander Bernstein | 3 | MA030169 | ||
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Geostatistics and Reservoir Simulation
The course includes lectures in reservoir simulation, history matching, and fundamentals of geostatistics.
Reservoir simulation and history matching embrace the following: 1) Fundamentals of single-phase and multiphase multicomponent fluid flow and storage in reservoirs 2) Numerical solution of governing equations using finite difference 3) Introduction to inverse theory and history matching 4) Simulating of laboratory PVT data by Equation Of States (EOS) Fundamentals of geostatistics include the following: 1) Stochastic reservoir simulation 2) Statistical measures 3) Univariate and multivariate Statistics 4) Covariance and variograms 5) Sequential Gaussian simulation 6) Uncertainty quantification Finally, reservoir simulation and geostatistical analysis are integrated for risk analysis and economy estimation. Laboratory computational exercises are also included. |
Dmitri Koroteev | 6 | MA060085 | ||
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High Performance Computing and Modern Architectures
High Performance Computing refers to accumulation and usage of computing power well beyond a typical desktop or laptop computer. This is a main course for various aspects of HPC and a further development of the Scientific Computing course. Together with the theoretical part and discussion of basic parallel algorithms, the course will have a practical component aimed at solving different research and industry-related problems on different computing architectures, such as modern CPUs and GPUs. The course will provide sufficient knowledge and experience in using standard parallel libraries (such as OpenMP, MPI and CUDA) as well as visualization software (ParaView, Visit). Students will be given a chance of using Skoltech's world-class HPC facilities to learn typical methods and rules of working on the large-scale collectively used supercomputers. The course is designed in such a way that students who successfully pass the exam will be able to use advanced methods of HPC in their everyday work.
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Sergey Rykovanov | 6 | MA060287 | ||
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Hybrid Photonics Computing
Hybrid Photonics computing is an interdisciplinary subject that takes inspiration from physics, mathematics, computer science, and electronic engineering to design artificial neural systems capable of solving hard optimisation problems. Recently various neural network systems emerged as a promising alternative to universal classical or quantum computing and to quantum simulators/annealers. Their physical implementation is based on dynamics of so-called coherent centers in the network of lasers, optical parametric oscillators, cold atomic gases, exciton-polariton condensates, nanolasers, memristors, VO2 oscillators, fibers, etc. They are expected to serve as fast and accurate accelerators for modern digital computers in the specialized tasks for integer and continuous optimization problems in vastly different areas such as vehicle routing and scheduling problems, dynamic analysis of neural networks and financial markets, prediction of new chemical materials and machine learning. The theoretical framework of hybrid photonics systems was proposed as heuristic algorithms for NP-hard optimization problems and efficient simulators of many-body systems. This course covers fundamental principles, algorithms, and applications, as well as the physical implementation of photonic computing.
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Natalia Berloff | 3 | MA030337 | ||
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Immunology
The purpose of the course “Immunology” is to lay the foundation for understanding the principles of organization, basic algorithms and rationale of the immune system. Such basis is necessary for the further professional growth either in the field of fundamental immunology or applied research and development in medical immunology. This course will also be important for those who wish to professionalize in medical practice, pharmaceutical industry, epidemiology and health services management, engineering and business in the field of modern biomedicine.
The course is focussed on the human immune system, towards clinical applications. The first part should help to form a systematic view of the architecture of the immune system. Next, the main medical aspects related to the functioning of the immune system will be considered, including autoimmune diseases, hypersensitivity reactions, cancer immunology, immunotherapy, vaccinations, autologous and allogeneic blood cells transplantation. Special attention paid to adaptive immunity and state-of-art bioinformatic approaches to the analysis of the T-cell receptor and antibody repertoire, the search for diagnostically and therapeutically relevant markers of adaptive immunity. In addition to the main program, PhD students and the boldest MS students will be offered to run a short, team-up, publication-oriented biomedical project. |
Dmitriy Chudakov | 6 | MA060172 | ||
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Intellectual Property, Technological Innovation and Entrepreneurship
The successful development of innovative technology ventures depends substantially on how well their intellectual property (IP) assets are protected, managed qnd leveraged. For technology entrepreneurs, skill in the management of IP is at least as important as skill in managing technology, people, organizations and business.
It is almost impossible for engineers or scientists to avoid confronting issues related to intellectual property. These issues include: the risk of violating the IP rights of others; an obligation to respect the IP policies of one’s employer; the need to obtain IP protection for one’s own inventions and creative works; the obligation to become involved in the management of the IP belonging to one’s employer; generating strategies for extracting value from one’s intellectual assets; and the challenge of ensuring that one’s own IP rights are not infringed by others, including by one’s own employer or one’s clients. In addition, given that such a large amount of contemporary business—in both the private sector and government—involves outsourcing and inter-organizational collaboration, expertise in the licensing of intellectual property rights is in high demand. The management of intellectual property may often also involve artfully connecting proprietary strategies with open innovation strategies. This course will survey basic concepts of intellectual property and provide an introduction to a variety of types of intellectual property and IP-related rights, such as patents, copyright, trade secrets, trademarks, design rights, database rights, domain names, and demarcations of origin. The course will also examine the strategic management of IP in the process of technology commercialization, and the resolution of IP-related conflicts between technology-based enterprises. It will place special attention on the IP challenges faced by entrepreneurial technology ventures. |
Kelvin Willoughby | 6 | E&I | MC060027 | CANCELLED |
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Introduction to Quantum Theory (Term 3-4)
One of the most striking breakthrough of the XX century is the creation of the entirely new area of physics named quantum physics. It emerged that the whole world around us obeys the laws of quantum mechanics, while the laws of classical physics that we are familiar with (such as, for example, Newton's equations) describe only macroscopic objects and can be obtained in limiting case. After that a lot of phenomena in different areas of physics found their explanation. Also quantum mechanics had a very
significant impact on the development of mathematics and mathematical physics. Today quantum mechanics is one of the keystone parts of theoretical and mathematical physics. |
Vladimir Losyakov |
63 per term
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MA060332 | ||
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Machine Learning for Wireless Communications
This is a machine learning application course, intended to familiarize students with modern algorithms of the 5G wireless communication system and their implementation over Machine learning (ML). Within a few years, ML has become a prominent and rapidly growing research field among wireless communications both in academia and industry. The application of ML to wireless communications is expected to deeply transform wireless communication engineering in a few years. ML brings along a methodology that is data-driven and research in the field of ML for 5G is still largely in an exploration phase. In this course, we analyze the most promising applications of ML in the 5G system and propose students to realize some of them in Matlab or Python using the real-life data and state-of-art algorithms we provide.
This course covers the following topics: Machine learning-based feature extraction for channel estimation, and MIMO detection |
Andrey Ivanov, Dmitry Yarotsky |
3 | MA030413 | ||
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Machine Learning in Structural Bioinformatics and Chemoinformatics
Deep learning achieves remarkable results in many fields, including life sciences. This course is made for students, that ready to apply their skills to scientific applications, such as biomolecular design, analysis, and drug discovery. Molecules play vital roles in our organism, constantly interacting with each other and serving all the functionality, that we have as human beings. Prediction of molecular properties, as well as the design of molecules with target properties, are highly important problems, that still need to be addressed. Complex and rich nature of molecules allows us to represent them as sequences, graphs, 3D objects, or high-dimensional descriptors, and to apply numerical methods in order to solve open problems in structural bioinformatics and chemoinformatics. Rapid accumulation of molecular data opened gates for machine learning to be applied for such representations and to derive powerful prediction models that outperform heuristical methods.
During this course students will be introduced with open problems in structural bioinformatics and chemoinformatics and with state-of-the-art machine learning methods attempt to solve these problems. Particularly students will practice machine learning for drug design and biomolecular analysis problems. The course includes theoretical lectures, that cover a basis for molecular structures, such that no prior knowledge of structural chemistry or biology is required. Seminars are python coding sessions, where students apply machine learning pipelines to derive prediction models. The end of the course comprises the capstone project aimed to solve the structural bioinformatics and chemoinformatics problem of choice using machine learning. |
Petr Popov | 6 | MA060471 | ||
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Master Your Thesis in English 2 (Term 7-8)
This is Spring Module of the Course.
The key to efficient professional communication is the ability to convey ideas clearly, coherently and correctly both orally and in writing. |
Elizaveta Tikhomirova |
31.5 per term
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Extra | MF030004 | |
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Methods of Enhanced Oil Recovery
Over one-half of the original oil-in-place remains in the reservoirs as primary and secondary recovery techniques have its technological and economic limitations. Major reasons are:
• heterogeneity of the reservoirs; • unfavorable fluid properties; • inefficient nature of the displacement process; economic constraints. A better understanding of the reservoir fundamentals and the important variables that influence the recovery process can enhance oil recovery. This course presents a comprehensive summary of chemical, miscible, and thermal enhanced oil recovery processes. The course presents the subject material with a clear focus on developing and producing the reservoir efficiently, energies available within the reservoir, realizing technical benefits and application limitations of the various enhanced oil recovery methods. |
Alexey Cheremisin | 6 | MA060117 | ||
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Modern Dynamical Systems (Term 3-4)
Dynamical systems in our course will be presented mainly not as an independent branch of mathematics but as a very powerful tool that can be applied in geometry, topology, probability, analysis, number theory and physics. We consciously decided to sacrifice some classical chapters of ergodic theory and to introduce the most important dynamical notions and ideas in the geometric and topological context already intuitively familiar to our audience. As a compensation, we will show applications of dynamics to important problems in other mathematical disciplines. We hope to arrive at the end of the course to the most recent advances in dynamics and geometry and to present (at least informally) some of results of A. Avila, A. Eskin, M. Kontsevich, M. Mirzakhani, G. Margulis.
In accordance with this strategy, the course comprises several blocks closely related to each other. The first three of them (including very short introduction) are mainly mandatory. The decision, which of the topics listed below these three blocks would depend on the background and interests of the audience. |
Alexandra Skripchenko, Sergey Lando |
63 per term
|
MA060257 | ||
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Modern Plant Breeding Workshop
This teaching unit is built around "The Plant Breeding Project", a common thread throughout the course carried out by 'plant breeders teams' (typically 2-3 students). This project focuses on the genetic improvement of a crop for agronomic traits of interest. The goal is to propose a strategy for improving the species for the considered traits and to discuss the possible alternatives.
To do so, the students will analyse real large-scale phenotypic and genomic data sets while implementing suitable and detailed bio-statistical and quantitative genetic methods and relying on scientific publications presenting the most up-to-date and groundbreaking AgBioTechs available for the studied crop. "Resources" course and hands-on, allowing a concrete application of the concepts taught in the context of case studies, will provide the knowledge and skills necessary for carrying out the project. These sessions will deal with the analysis of Genotype X Environment X Management (GXEXM) interaction, landscape genomics, genomic selection, envirotyping, and other prospective and groundbreaking approaches in plant breeding such as high-throughput (HT) phenomics, plant breeding digitalization, … Creativity sessions using design thinking methods (e.g. group brainstorming) will make it possible to reflect on the challenges, needs, and avenues of innovation for the crop and agri sector concerned, as well as on the definition of key selection targets, strategy and possible alternatives that can be implemented for a program to improve the plant species considered. |
Cecile Ben | 3 | MA030486 | ||
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Multi-Scale Mechanics of Materials
This course provides a broad base treatment of the core subject of mechanics of materials considered at different organisational and structural scales, from atoms to nanocrystals to micron-sized grains and solid phase particles, and ultimately to millimeter to meter-sized objects and assemblies.
The two-track approach taken in this course will systematically combine theoretical descriptions appropriate for the analysis at different scales with the corresponding experimental implementations, measurement and observation techniques, and interpretation approaches. At the macroscopic level, classical mechanics concepts of stress and strain will be introduced. The continuum mechanics modelling frameworks for elastic-plastic deformation will be presented, along with large strain descriptors. Mechanical testing methods will be described, and the importance of Digital Image Correlation (DIC) will be highlighted. Fracture mechanics will be introduced in the context of linear elastic and least-plastic behaviour. The framework for modelling crystal plasticity will be presented, and examples drawn from EBSD and Laue micro-beam diffraction analyses. Nanomechanics of materials will be presented, along with nano indentation and other small scale mechanical testing methods. At the atomic level, first principles and molecular dynamics approaches will be touched, and AFM, TEM and atom probe characterisation methods will be introduced, along with geometric phase analysis and 2D Fourier transform methods of interpretation. The concept of hierarchically structured materials will be presented, and examples discussed drawn from natural and engineered materials. |
Alexander Korsunsky | 3 | DA030496 | MOVED FROM T2 | |
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Nanocomposites
The focus of the course is a special class of composites which include nano-scale reinforcements. Such nano-reinforced materials can be classified in two groups:
1. Nano-composites which have reinforcing phase of nano-dimensions (carbon nanotubes (CNT), graphene and graphene-related materials (graphene oxide etc), nano-clays and some others. For both types of nano-composites the course covers their production, microstructure, micromechanics, functional properties (electrical and thermal conductivity) and applications. There has been immense interest in the use of carbon nanomaterials for reinforcement of plastics and their composites in the recent years. The course provides to Skoltech students an opportunity to catch with this accelerated trend of world-wide research. |
Sergey Abaimov | 6 | MA060329 | ||
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Neural Natural Language Processing
The course is about neural models for natural language processing. The new generation of neural network-based methods based on deep learning has dramatically improved the performance of a wide range of natural language processing tasks, ranging from text classification to question answering. The course covers the basics and the details of successful models and methods for natural language processing based on neural networks, starting from the simple word embedding models, such as word2vec, all the way to more sophisticated language models, such ELMo and BERT. Besides, the course contains a small introduction to basic NLP methods. The course involves a substantial practical component with a number of practical assignments.
|
Alexander Panchenko | 3 | MA030361 | ||
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Neuromorphic Computing
Over the past decades the concept of neuromorphic computing that relies on imitating and exploiting the mechanisms inherent to biological nervous system has evolved into an interdisciplinary research area at the boundary between advanced computing and computational neuroscience. This direction is largely considered as one of the most promising approach to resolve the critical problems that come with continual miniaturization and ever-increasing power consumption of CMOS technology. A vast number of brain-inspired algorithms and architectures, endowing low-power requirements and massive parallel computing principles, have been attempted for applications, including complex pattern recognition, image processing, and data mining. In parallel, intensive research has been conducted for practical implementation of learning-based artificial synapses and neurons that represent two fundamental building blocks of biological neural networks.
The course is designed to provide the students with basic understanding and familiarize them with recent achievements in the field of neuromorphic engineering as implemented in artificial and spiking neural networks. In the course we will address: (i) Mathematical modeling of neurons with synapses and cognitive processes; (ii) Artificial neural networks and spiking neural networks; (iii) Temporal encoding and learning in spiking neural networks; (iv) Overview of available hardware architectures for neuromorphic computing; (v) Memristor-based neuromorphic computing. |
Dmitry Yudin | 3 | MA030407 | ||
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Neurophysiology and Neurotechnologies
Neurophysiology deals with neuronal mechanisms that govern various functions of our bodies, from motor control and somatosensory sensations to cognition. With the development of new methods, such as neural recording techniques, decoding algorithms and robotics, neurotechnologies have started to emerge that have practical significance as treatment and rehabilitation approaches to severe neural conditions. Thus, brain-computer interfaces decode brain activity and link it to assistive devices, such as prosthetic limbs and communication systems. Given these developments, educating a new generation of experts in neural technologies has become important for the advancement of science, technology and medicine, with a considerable economical impact. In this course, students will start with learning key neurophysiological concepts. They will then learn how these concepts are applied to the development of modern neurotechnologies for clinical applications and the applications for augmenting brain functions in healthy people. Course prerequisites include very basic knowledge of physiology, neurophysiology and mathematics at an undergraduate level.
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Mikhail Lebedev | 6 | MA060488 | ||
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Omics Data Analysis (Term 3-4)
An avalanche of ‘omics data is coming from different sources: transcriptomics, epigenomics, lipidomics, metabolomics. A thorough analysis of such large-scale biological data sets can lead to the discovery of important biological insights on mechanisms of cell and organ functioning, and to the identification of small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions. The course will discuss general concepts of bioinformatics analysis of various types of ‘omics data and will provide examples of their successful application for solving a wide range of biological problems. The students will not only learn the best analysis practices suitable for each data type but, importantly, understand why each analysis step is necessary, what is the logic of the whole data analysis concept, which controls are essential, etc. The course will end with the task (Final Project) on the integration of different data types produced to answer the same biological question.
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Ekaterina Khrameeva |
63 per term
|
MA060061 | ||
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Omics Technologies
Course "Omics Technologies" includes different data intensive disciplines dedicated to the molecular profiling of various natural or biological systems: genomics, transcriptomics, proteomics, metabolomics and lipidomics. This course will be mainly focused on the mass spectrometry based techniques refereeing genomics to the special Live Science courses: "Instrumental methods in Molecular Biology" and "Analysis of ‘omics data". The base laboratory of the course is the "Omics Technology and Big Data for Personalized Medicine and Health laboratory" (C. Borchers, supported by Megagrant of Ministry of Science and Higher Education of the Russian Federation).
The course covers wide range of mass spectrometry techniques used for ion generation, separation, detection, data processing and interpretation. The course provides the theoretical fundamentals required for choosing of the instruments and methods for measuring mass spectra of biological samples and the quantitative proteomics and metabolomics mass-spectrometry-based assays and its application in biomedical and clinical science. The course covers Big Data processing and Machine Learning approaches used for the biomarker discovery and tissue imaging. After successful completion of this course, students will acquire the initial knowledge of the operational principles and design of different mass spectrometers, different method of protein, peptides, lipids and metabolite molecule identification, different fragmentation methods for primary and secondary structure determination, methods of quantitative determination of proteins, lipids, metabolites and small molecule in physiological liquids and applications of this methods in clinical science and practice. |
Evgeny Nikolaev | 6 | MA060360 | ||
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Optical Communications (Term 3-4)
This course is the first part of a series devoted to modern optical communication systems. The aim of the course is to study the fundamental physical principles underlying modern optical communication systems. Further courses in this series will be devoted to more applied (engineering) issues in the field of optical information transmission systems. The proposed course will give the necessary knowledge from the theory of information, universal for any (not only optical) data transmission systems, in particular the Shannon theorem. Based on a unified description of stochastic processes, sources of errors in digital signal transmission will be systematically considered. Fundamentals of quantum mechanics of open systems based on density matrix will be systematically described. Using this approach, the dynamics of lasers and optical amplifiers as applied to optical information transmission systems will be analyzed. The main factors limiting the capacity and distance of the transmitted signal will be considered. In particular, the linear and nonlinear properties of the medium (mainly on the example of optical fibers) that affect the parameters of the propagating signal will be analyzed. The course will also consider modern issues of physics and technology of information transmission systems, including quantum methods to ensure the confidentiality of the transmitted signal, applications of nanophotonics and photonic integrated circuits, neural optical networks, radio photonics, as well as the role of photonics in the development of mobile networks of the fifth and sixth generations.
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Arkady Shipulin |
63 per term
|
MA060157 | ||
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course
describes the intended learning outcomes and how they are assessed. The main bulk of the 81 hours of the course is spent in the actual courses in which |
Dmitry Artamonov | 3 | DG030005 | ||
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Pedagogy of Higher Education (Term 3-4)
The course offers an introduction to facilitating learning in higher education for PhD students asked to act as teaching assistants. The course content focuses on high resolution constructive alignment of learning outcomes with learning activities and assessment strategies. Learning outcomes for a course are elaborated into separate activities and assignments for students. Learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. Participants in the course will therefore be required to plan and design effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments. The course therefore emphasizes communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course explores ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching assistantships or their supervisory activities to come. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson |
31.5 per term
|
DG030025 | ||
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Plant and Animal Quantitative Genetics
Traits such as height, weight, blood pressure, or longevity vary greatly among individuals and have continuously distributed phenotypes within populations that do not show simple Mendelian inheritance. Likewise, most economically important traits in crops (such as yield, root architecture, seed proteins and fatty acids content, photosynthesis efficiency…) and livestock species (litter size, milk production, meat organoleptic properties) are quantitative rather than qualitative.
Quantitative genetic variation is the substrate for phenotypic evolution in natural populations and for selective breeding of domestic crop and animal species. Quantitative genetic variation also underlies susceptibility to common complex diseases and behavioral disorders in humans, as well as responses to pharmacological therapies. As such, a thorough understanding of the underlying genetic control of these traits can help alleviate complex diseases and develop new and more personalized therapeutic interventions to improve human health. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on a model in which many genes control the trait and in which non-genetic environmental factors may also influence. During this course, implemented in a skill-based learning approach based on analysis of real data sets and case studies, and team-based projects, the students will learn and practice the most up-to-date computational and statistical methods in quantitative genetics, from high-density genetic maps and Quantitative Trait Loci (QTL) mapping to GWAS (Genome-Wide Association Studies) and Genomic Prediction. |
Laurent Gentzbittel | 3 | MA030485 | ||
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Power Markets and Regulations
The course will introduce the students to power system economics. After covering the fundamentals of microeconomics, the main types of electricity markets and regulations will be discussed including the Russian market. Economic dispatch and Optimal Power Flow with Locational Marginal Pricing will be covered. Capacity planning, ancillary services, and risk analysis are also covered.
The lectures will be supplemented by homeworks utilizing PowerWorld simulation package, a laboratory exercises investigating gaming in power markets and group mini-projects. |
Janusz Bialek, David Pozo |
6 | MA060441 | ||
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Practicum in Experimental Physics 2
This course assumes mastering in certain experimental techniques in physics, including a practical work with experimental setups. The course is practically oriented, with small share of lectures. Students will have an opportunity to conduct individual research project and be familiar with unique state-of-the-art equipment.
The work can be starteded in Term 4, or can be continued after participation in Term 2. For Skoltech-MIPT net program, both Term 2 and Term 4 are obligatory from MIPT side, but can be substituted with other courses in frames of individual MIPT plan. |
Valery Ryazanov | 6 | MA060445 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Maxim Fedorov | 3 | DD030020cd | ||
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Qualifying Exam: Engineering Systems
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Clement Fortin | 3 | DD030020es | ||
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Qualifying Exam: Mathematics and Mechanics
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Igor Krichever, Iskander Akhatov |
3 | DD030020mm | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Dimitri Pissarenko | 3 | DD030020pe | ||
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Quantum Field Theory (Term 3-4)
At present time Quantum Field Theory (QFT) is the main theoretical tool used for the description of the phenomena occurring in the microworld. Examples include interactions between elementary particles, hadron structure and so on. At the same time, QFT methods are widely used in all areas of modern theoretical physics such as condensed matter physics, statistical mechanics, turbulence theory and others. Moreover, the creation of QFT has stimulated the development of many modern areas of mathematics.
The course is aimed at the study of the basic ideas and methods of QFT, as well as the discussion of its applications in various areas of modern theoretical and mathematical physics. Topics include quantization of scalar and gauge theories, path integral approach, perturbative expansions and Feynman diagrams, (1+1) dimensional exactly soluble models and some other ideas of modern science. |
Andrei Semenov |
63 per term
|
MA060316 | ||
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Quantum Integrable Systems (Term 3-4)
The course is devoted to quantum integrable systems. The history of quantum integrable systems starts from 1931 when
H.Bethe managed to construct exact eigenfunctions of the Hamiltonian of the Heisenberg spin chain with the help of a special substitution which became famous since that time (ansatz Bethe). In one or another form this method turns out to be applicable to many spin and field-theoretical integrable models. From the mathematical point of view, Bethe's method is connected to representation theory of quantum algebras (q-deformations of universal enveloping algebras and Yangians). Here is the list of topics which will be discussed in the course. – Coordinate Bethe ansatz on the example of the Heisenberg model and – Bethe ansatz in exactly solvable models of statistical mechanics – Calculation of physical quantities in integrable models in thermodynamic – Bethe equations and the Yang-Yang function, caclulation of norms of Bethe – Quantum inverse scattering method and algebraic Bethe ansatz, quantum R-matrices, – Functional Bethe ansatz and the method of Baxter's Q-operators, functional The knowledge of quantum mechanics and statistical physics for understanding of |
Anton Zabrodin |
63 per term
|
MA060315 | ||
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Quantum Optics
“Quantum optics, the union of quantum field theory and physical optics, is undergoing a time of revolutionary change” [Marlan O. Scully and M. Suhail Zubairy “Quantum Optics”, Cambridge University Press].
Quantum optics studies interactions between matter and the radiation field where quantum effects are important. The fundamental interest in quantum optics is connected with conceptual foundations of quantum mechanics, with non-classical effects such as quantum interference and entanglement, photon antibunching and squeezing, as well as with numerous applications in precise measurements, protected information transfer, etc. This introductory course includes the following topics: quantization of electromagnetic field, Fock (number) states of the field, Lamb shift, Casimir effect, coherent states of the field, interaction of photons with atoms, Rabi and Jaynes – Cummings models. Dressed states. Dicke super- and subradiation, quantum coherence and correlation measurements, quantum-mechanical detector of photons, single-photon interferometer, quantum beam splitter, Young’s type interferometer, Michelson’s stellar interferometer, physics of Hanbury-Brown-Twiss interferometer, non-classical states of light, squeezing in nonlinear optical processes, bunching and antibunching of photons, “Schroedinger’s cat” states. Elements of quantum computing |
Vladimir Yudson, Yulia Vladimirova |
3 | MA030161 | ||
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Research Methodology: Bioinformatics
The course consists of two main components: discussion of typical approaches and problems common to different areas of bioinformatics and work with bioinformatics texts, such as writing papers, responding to reviewer comments, reviewing papers, writing grant proposals, etc. Both parts will be based on actual cases; in the first stream the students will analyze particular problems and apply generic rules (or spot inconsistencies in application of these rules), in the second stream the students will review actual papers and analyze reviews by others, write and review grant proposals, analyze published retractions and the reasons for them, and so on. In particular, we will systematically analyze bioinformatics papers on SARS-CoV-2.
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Mikhail Gelfand | 3 | DA030404 | ||
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Research Methodology: CDMM Research Seminar (Term 2-4)
This is the main research seminar for the Skoltech Center for Design, Manufacturing and Materials (CDMM). All MSc students either enrolled into the Master Program in Advanced Manufacturing Technologies or PhD students affiliated with CDMM should attend this seminar. The format of the seminar is weekly invited lectures from top scientists in the research fields related to Advanced Manufacturing, Digital Engineering Technologies, and Mechanics and Physics of Advanced Manufacturing will be given.
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Iskander Akhatov |
31 per term
|
DG030102dm | ||
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Research Methodology: Space Center Seminar (Term 1B-4)
The seminar will cover current topics in the space domain: latest news, discoveries. Also planned that all PhD students and some Master students will present their research. External lecturers will be invited to focus on the main applications of space technologies: science, telecommunication, navigation and remote sensing. Aspects of space technologies will also be discussed: structures, software, attitude determination and control systems, on board computers, communication system power supply systems and others. The seminar will be offered in English.
|
Anton Ivanov |
30.75 per term
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DG030102es | ||
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Research Seminar "Advanced Materials Science" (Term 2-4)
This is the main research seminar of the Skoltech Center for Electrochemical Energy Storage and Materials Science Education program featuring presentations of young researchers: MSc students, PhD students, postdocs. Every MSc and PhD student of Materials Science program should deliver at least one presentation per two years. The range of topics is broad and includes any aspects of materials science and engineering.
Please see the seminar webpage at http://crei.skoltech.ru/cee/education/wednesday-scientific-seminar/ |
Keith Stevenson |
1.50.5 per term
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DG030302i | ||
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Research Seminar "Energy Systems and Technologies" (Term 2-4)
This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8).
Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Evaluation. Filling in the Online feedback form. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. |
Elena Gryazina |
31 per term
|
MA030489 | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1B-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and their theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, etc.
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Pavlo Gavrylenko |
61.5 per term
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DG060268 | ||
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Review of Materials and Devices for Nano- and Optoelectronics 2
This is the second part of 'Review of materials and devices' started in Term 3. The lectures are presented by the scientists working actively in various directions of nanoelectronics and optoelectronics in Russia and abroad. Seminars assume the discussion of recent original papers in the area, as well as the classical papers presenting a physical basis for devices operation. Any student can take either part 1 (Term 3) or part 2 (Term 4) separately, with one presentation of paper. Alternatively, the student can take both parts and present two papers, one in Term 3 and jne in Term 4.
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Valery Ryazanov | 3 | MA030334 | ||
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Safety Aspects of Artificial Intelligence
AI-based applications occupy ever more areas of human activity. The related machine learning methods have been enjoying a new momentum with the broadening application of deep and convolution artificial neural nets. TensorFlow played an important role in this trend as it greatly eased implementation of deep neural nets. Despite these positive trends, there are still gaps in AI methodologies, primarily related to safe operation. When it comes to, say, image classification on a web platform, system safety might be not in the foreground, but it is indeed in an autonomous car, a robot or unmanned aircraft or ship. The Stanford Center for AI Safety has recently raised these concerns in a respective white paper. It seems we are standing at an emerging interconnection of various disciplines that may come handy in machine learning for guaranteeing stability, robustness and user certificate satisfaction.
In this course, we tackle these challenging questions by looking at neural networks from an interdisciplinary point of view. Machine learning is traditionally concerned with the neural network performance, say, in terms of weight convergence and predicting ability. In system theory, we might look at the neural network learning as being a dynamical plant with well-definable properties, such as stability or input-to-output robustness. Or, we may interpret it in a formal setting of some axiomatic system and pose such questions as: "Does the network produce correct output provided with so or so input?". In fault-tolerant control, in turn, one might want to detect, e.g., an activation function fault and remedy it. And so on … Thanks to view from various perspectives, you will not just expand your knowledge of AI-based methods, but enrich and broaden your understanding of neural network functioning, as well as get better fit for the new generation of safe AI. |
Pavel Osinenko | 3 | MA030419 | ||
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Selected Topics in Data Science
In this course, we introduce the forefront of modern research in data science and familiarize Ph.D. students with state-of-the-art works in those areas. In particular, we introduce cornerstone subjects that are not commonly discussed in undergraduate or graduate Machine Learning classes. This course is intended to serve as an introduction to the basics of everyday industrial application of Machine Learning and Software Engineering.
The course explores a wide range of topics from how to implement your data science solution into production and measure its performance to intricacies of some specific problems such as recommendation systems, uncertainty estimation, visual search, churn/Customer Lifetime Value and other applications. The course aims to bring all students on the same page regarding the nature and orientation of state-of-the-art work in their field so that they acquire both depth and breadth of knowledge. |
Evgeny Burnaev | 6 | DA060492 | ||
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Selected Topics in Energy: Physical, Chemical and Geophysical Challenges (Term 2-4)
The course provides an introduction to the modern topics related to fundamentals of exploration of energy resources, energy generation, storage, conversion and use. It identifies the corresponding practical challenges to be addressed at the fundamental research level and familiarizes the students with the state-of-the-art approaches, methods and techniques in use in related scientific areas. The course seeks to emphasize and maintain interdisciplinary nature of the energy-related topics, in particular, combination of micro- and macroscopic approaches of geophysics, mechanics and chemistry in hydrocarbon exploration and development, relation between the physical and chemical processes of energy generation and conversion, integration of physical, chemical and mechanical approaches to perspective materials (physical and chemical synthesis, micro- and macroscopic characterization, structure-property relations, etc.) and related theoretical methodologies. These interdisciplinary links are mostly demonstrated by horizontal knowledge exchange among the students reporting and discussing practical examples from their own research field or from modern review or research publications. Topical lectures are included for further exploration of these links. The secondary aim of the course is the development of presentation skills (oral and writing), as well as scientific peer-review experience. The seminar format chosen for most activities allows students free exchange of knowledge and ideas, broader vision of their research projects and methodologies, better assessments of their own research skills and demands for further education.
|
Alexei Buchachenko |
62 per term
|
DG060106 | ||
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Smart Grids
Power systems around the world are undergoing a period of unprecedented change. A typical 20th Century power system was characterized by unidirectional flow of power from a limited number of large controllable power stations to a highly predictable demand. There was no energy storage so that at any time generation had to be equal to demand and the infrastructure utilization rates were low (about 55% for generation, 30% for transmission and even lower for distribution). Generally planning and controlling such a system was relatively straightforward as it was based around principles of deterministic hierarchical control, usually based on (N-1) reliability criterion.
On the other hand the emerging 21st Century power system is characterized by bidirectional flows between a very large number of uncontrollable and stochastic generators (usually, but not always, renewable ones such as wind or solar) and stochastic and often poorly-predictable demand. Demand ceases to be predictable as it consists of consumers equipped with smart meters and wind/solar generators hence possibly becoming net generators – so-called prosumers. Increased penetration of energy storage, both stationary and mobile due to a take-up of electric vehicles, offers buffering possibilities in dispatch (generation does not have to be equal to demand at any time). Controlling such a power system is the main research challenge in power systems and it is made possible by latest advances in ICT (Information and Control Technology), communication networks, Internet, GPS, sensors, etc. However it requires new tools and methodologies, the Smartgrid course will give the basis of this new grid scenario. |
Federico Martin Ibanez | 6 | MA060056 | ||
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Superconducting Quantum Technologies
This course provides an overview of the rapidly developing field of physics of superconducting quantum systems. The course gives an introduction to basic phenomena of on-chip quantum optics in the microwave range, explain the quantum mechanical approach to superconducting circuits, study the interaction of electromagnetic waves with artificial atoms and provide examples of theoretical understanding and experimental realizations of these effects. Prior knowledge of quantum theory at the level of bra-ket notation and quantum evolution is assumed. Knowledge of superconductivity is not required.
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Oleg Astafiev | 6 | MA060340 | ||
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Technological Innovations: from Research Results to a Commercial Product
The course is about managing applied Research and Development, critical analysis of new emerging technologies, transferring research results and discoveries into successful products.
Research and development process in a company or in a technical university is usually application and product oriented. There are usually no universal rules on how to distinguish a commercially perspective research result / technology from many others; neither there are standard paths for commercializing scientific results. Intuition and skills come with practice. So, in this course, practical skills and experience will be developed. Students will go through multiple real cases of successful commercialization of research results in various technological fields – materials science, nanotechnology, photonics, space, chemistry, data science, energy etc. They will see and practice in how a research result from a laboratory can be transferred into a product, find its customer and market and, finally, become commercially successful. Opposite situations (that are, in fact, more common) will be studied equally carefully: when originally promising technologies did not make its way into a product – due to multiple reasons that will be analyzed. The lecturers will be balanced with practical work. The real-life conditions will be modelled as much as possible – aimed to develop hands-on skills and experience in critical evaluation of new technologies, comparing them to existing alternative solutions, finding a proper product realization and a market niche. Students are expected to go through the technological innovation process themselves and then to compare, when possible, their results to the real stories of successes and failures. Students will apply their learnings to their current research – considering their own results and/or results of their colleagues and collaborators from the point of view of commercialization potential, finding right target market, realizing a competitive product. |
Pavel Dorozhkin | 3 | E&I | MC030016 | |
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Technology Planning and Roadmapping: Advanced
• Technology Planning and Roadmapping: Advanced (TPR:A): this course represents the practical application of the tools taught in TPR:F. It provides students the opportunity to practice hands on the real issues that arise in implementing a TPR system in industrial organizations, and to develop an actual technology roadmap in class team-work. The best technology roadmaps coming from different class editions may be published online or in international peer-reviewed venues, with students as lead authors (Scopus-indexed conferences or journals). TPR:A is about using the TPR system to explore a cutting-edge technology area of choice of the class, among those aligned with major trends occurring worldwide across different technology sectors of relevance to Skoltech (Biomed, Energy, IT, and Space). The main deliverable of TPR:A is a group-based technology roadmap report. Students will develop in teams a sector-wide technology roadmap, to be later presented as a report.
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Alessandro Golkar | 3 | E&I | DC030018 | |
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Thermodynamics and Transport at Nanoscale
The course introduces students to basic principles of thermodynamics and transport in “small systems, that is, the system whose size is comparable to the size of its major elements (be those molecules, micelles, polymer coils, etc). It covers small droplets, bubbles and crystals, stability of thin films, adsorption and deformation in nanoporous materials, stability of nanocolloids and nanocomposites, as well as transport and flows in micro-and nano- porous networks. In the process of studies, the students will also learn/reiterate the basics of interfacial thermodynamics, adsorption, wettability, spreading as well as the basics of transport of ionic and nonionic compounds in confined media. The lectures, seminars, homeworks and the final project will give practical example of calculations for all major classes of nanomaterials: nanoparticles and nanocomposites, microporous crystals, ordered and disordered mesoporous solids, and, finally nanostructured polymeric materials.
The course will be useful to all students willing to improve their understanding of natural (e.g. mineral oil) and man-made colloids, such as suspensions, emulsions and foams, as well as natural and synthetic porous materials. "Lectures" in the schedule refer to approximately 3h "windows" they will are not necessary scheduled as such, but will spread out and intermitted by seminars / recitations / HW and project discussions |
Alexey Vishnyakov | 3 | MA030288 | ||
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Thesis Final Review
The Thesis Final Review is an assessment procedure that aims to determine whether a PhD student’s work is eligible for the PhD thesis defense.
The procedure also performs the function of the final state attestation. As a result of the Thesis Final Review the following decision can be made: Format: Scientific report in a format of PhD thesis draft and presentation. |
Nadezhda Dontsu |
6 per term
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DD480037 | ||
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory 6 credits component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 | ||
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Transgenic Models in Drug Discovery
The course consists of theoretical and practic parts.Theoreticla part is devoted to analysis of transgenesis in C.elegance, Drosophila, Zebra fish and mice with particular accent on the usage of these platforms in drug development. It also describes general principles of creation of genetically modified animals. Practical part enables students to obtain practical skills in all phases of the production of transgenic mice.
Transgenic animals are no alternative tool for studying gene function, modeling of human diseases, creating of animal-producing recombinant proteins for agricultural and pharmaceutical industries. Last years work on the creation of such organisms was intensified due to the widespread introduction of site-specific nucleases technology: "zinc fingers», TALEN, CRISPR / Cas9 types of nuclease. Increasingly, it can be heard about the creation of new models of diseases, the use of gene knockout for medical purposes. The course "Transgenic animals" allows students not only to get acquainted with the theory of molecular biological and embryological basis of modern approaches to the modification of the genome, but also to apply their knowledge during practical training. |
Yuri Kotelevtsev | 6 | MA060398 | ||
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Vertex Operator Algebras (Term 3-4)
Infinite-dimensional Lie algebras (such as Virasoro algebra or affine Kac-Moody
algebras) turn out to be very important in various areas of modern mathematics and mathematical physics. In particular, they are very useful in the description of some field theories. In this context one arranges infinite number of the Lie algebra elements into a single object called field. This idea generalizes to the general theory of vertex operator algebras. VOAs capture the main properties of the infinite diemensional Lie algebras and have rich additional structure. Vertex operator algebras proved to be very useful in many situations; the classical example is the KP integrable hierarchy. They are also extensively used in modern algebraic geometry. Our goal is to give an introduction to the theory of vertex operator algebras from the modern mathematical point of view. We describe the main definitions, constructions and applications of the theory. The course is aimed at PhD students and master students. |
Evgeny Feygin |
63 per term
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DA060259 | ||
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Virology
The course consists of two parts: Molecular Virology and Introduction to the Medical Virology. In the first part, the structure of viruses, their genetics as well as replication and transcription strategies will be explained. These aspects are crucial for understanding the second part of the course, which focuses on mechanisms of how viruses act on the whole organism and how organisms react to viral infection. Such topics as an immune response against viruses, types of viral infections, vaccines and antivirals will be covered in the second part of the course. Throughout the whole course, the world's most notorious pathogens such as Coronavirus, HIV, Ebola, Zika and Influenza, will be discussed in depth.
Lectures are based on the Virology course led by professor Racaniello at the University of Columbia with his kind permission. Lectures will be combined with seminars at which the papers important in the field will be discussed. After the completion of the course, students will know the history of virology, its current problems as well as directions for further development. |
Maria Sokolova | 6 | MA060374 | ||
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Virtual Reality, Augmented Reality and Haptics
VR and Haptics technologies are booming in the world. Students of the course will learn how to make 3D environment and digital twins. They will create the VR copy of the robot, control its motion with tracking system, and literally touch the future technologies with the most recent electrotactile display provided by UEC (Japan). They will learn how to program ESP microcontrollers, digital filters, and to write the software for tracking the user fingers, arms with motion capturing systems. They will be taught the neuroscience of the human sense of touch, principles of haptics technologies and tactile displays for highly immersive VR, HRI, and HCI. The experimental design with ANOVA test will be presented. At the end of the Lecture, the students will have the chance to participate in the scientific content of the most prestigious conference in CG and novel technologies ACM Siggraph 2020 (Core A*). The invited speakers will be from such companies as Unity, NVIDIA, AntiLatency, Native Robotics (startup of ISR Lab.), Tsuru Robotics (startup of ISR Lab.), Sizolution (startup of ISR Lab.).
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Dzmitry Tsetserukou | 3 | MA030456 |
Summer
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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Academic Communication: Preparatory English for Phd Exam
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. The course is aligned with the NATURE MASTERCLASS available to Skoltech researchers, so you will be able to benefit from professional recommendations of the Nature experts regarding the structure and contents of a publication, and constructive feedback from your Instructor on the language of your materials. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities, to be performed in class and at home, individually and in teams. By the end of the course, successful participants will – know the rules and conventions of research paper writing, including structure, style, grammar and vocabulary; – improve their academic communication skills, such as active listening, spontaneous and rehearsed speaking/ presentation, reading and writing within a given academic genre; – have experience in writing, editing, peer-reviewing and presenting research results. |
Elizaveta Tikhomirova | 3 | Extra | DF030029 | |
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Facilitating and Assessing Learning
The course offers an introduction to facilitating learning in higher education for junior faculty together with PhD student TAs. The course content focuses on aligning learning outcomes with learning activities and assessment strategies. Constructive alignment in the course is defined at high resolution such that learning outcomes for a course are elaborated into separate activities and assignments for students. In other words, learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. The assessment design that participants in the course design will therefore be required to reflect significant and effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments, and the course therefore comes with an emphasis on communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course contains discussion topics to highlight ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching and course development or their supervisory activities. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson | 3 | DG030030 | ||
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Industrial Immersion
The goal of Industrial Immersion is to provide for Skoltech students real hands-on work experience in industrial sector and develop the knowledge and skills for making impact through engineering and innovation. The Industrial immersion is performed in a company and it implies that internships at academic or research institutions (like universities etc.) are excluded. Duration of Industrial Immersion is 8 weeks. Project focuses on short-term development, manufacturing or operations challenges rather than long-term research problems and is co-supervised by the company and Skoltech. The internship is cooperatively planned: project assignment is provided by the company and subject for approval of the Industrial Immersion Program Coordinator (I.I.P.C.).
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12 | Sector | MB120005 | ||
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Pedagogy of Higher Education
The course offers an introduction to facilitating learning in higher education for PhD students asked to act as teaching assistants. The course content focuses on high resolution constructive alignment of learning outcomes with learning activities and assessment strategies. Learning outcomes for a course are elaborated into separate activities and assignments for students. Learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. Participants in the course will therefore be required to plan and design effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments. The course therefore emphasizes communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course explores ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching assistantships or their supervisory activities to come. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Magnus Gustafsson | 3 | DG030025 | CANCELLED | |
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Maxim Fedorov | 3 | DD030020cd | ||
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Qualifying Exam: Engineering Systems
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Clement Fortin | 3 | DD030020es | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. The Qualifying Exam consists of two parts: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Dimitri Pissarenko | 3 | DD030020pe | ||
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Research Immersion
Research Immersion will take place in Skoltech and Dubna as a part of Skoltech International Summer School on Mathematical Physics.
The program of the school includes modern topics of mathematical physics such as Topological strings, integrability, Schur-Weyl duality, Ising model, sigma models, Affine Grassmannian, Stochastic vertex models. Discussion of each topic will be divided into talks of participants. Preparation of the reviews of the subject of the talk, discussion of them with experts is an essential part of the school's work. After school, the participants will prepare a report based on the talk and further study. The main goal of the Research Immersion is an expansion of the professional knowledge gained by students and developing practical skills for conducting independent scientific work. Students gain experience in the study of an actual scientific problem, as well as the selection of the necessary materials for the performance of qualifying work – the master's thesis. |
Mikhail Bershtein | 12 | Sector | MB120006m | |
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Startup Founders Workshop
This is a “learning-by-doing” intensive course designed to provide science and engineering PhD students with a hands-on experience of translating your favorite technology into the innovative product and then the technology-based startup. The course will include lectures and individual mentoring sessions, as well as project-based activities covering all stages of the new venture creation: from validating the problem statement and “getting out of the building” (literally!!! your team is to talk to customers, partners, users, etc.), to defining the technological solution and the product, further to producing the prototype and landscaping the definitive business model.
The course is designed for wide diverse PhD student audience: The course flow is structured around 3 major stages (“Sprints”) comprising kick off intensive workshop, mentors and instructors supported projects development period, resulting capstone workshop and projects Demo Day. |
Dmitry Kulish, Alexey Nikolaev |
6 | E&I | DC060023 |