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The course starts with an overview of canonical machine learning (ML) applications and problems, learning scenarios, etc. and then introduces foundations of Bayesian approach to solve these problems. Bayesian approach allows one to take into account subject domain knowledge and/or user’s preferences through a prior distribution when constructing the model. Besides, it offers an efficient framework for model selection. We discuss which prior distributions types are usually used, limit properties of a posterior distribution, and provide some illustrations of the Bayesian approach.
The practical applicability of Bayesian methods in the last 20 years has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, as well as posterior simulation methods based on the Markov chain Monte Carlo approach. As a result Bayesian methods have grown from a specialist niche to become mainstream. Therefore, we devoted a second part of the course to approximation tools, vitally important for Bayesian inference, and provide examples how to use Bayesian approaches to automatically select features, tune the regularization parameter in regression and classification, etc.
The last part of the course is devoted to advanced Bayesian methods, namely, Gaussian Processes and deep Bayesian neural networks, which have become widespread in the last 5-8 years. We discuss deep Bayesian framework and then illustrate its applications through construction of deep variational autoencoders, approaches to variational dropout, Wasserstein Generative Adversarial Networks, deep Kalman filter, etc. Home assignments include solution of applied problems, development of modifications of Bayesian ML algorithms, and some theoretical exercises.
Invited medical oncologists will give talks on the usolved questions in the field. A visit to a clinic or a laboratory is planned
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 Field Theories and Sieberg-Witten solutions of N=2 Supersymmetric Gauge Theiories
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 short laboratory exercise. Hands-on tutorial 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. An overview of practical regularization recipes for these cameras will be given.
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 homeworks.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/
Foundation of continuum mechanics consists of: 1) material continuum model in the form of a deformable (with mechanical stresses and other macroparameters) continuous medium, described via several piecewise continuous differentiable functions. Building such a model is carried out by averaging the parameters of real materials that have a discrete atomic and molecular structure; 2) differential, integral, tensor calculus and the theory of dimensions with the fundamental idea of invariance under transformation of coordinate systems and systems dimension; 3) The laws of conservation of mass, momentum, angular momentum and energy, the laws of thermodynamics, expressed in terms of macroscopic parameters of the material continuum; 4) mechanical (rheological), thermal and electrical experiments that allow us to find connections between macroparameters of different substances at different mechanical, thermal, electromagnetic and physical-chemical processes. These representations constitute, in particular, the mathematical theory of thermo-electro-magneto-mechanical field.
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.
The Exam has two parts:
Part 1 – Pre-exam activities (Assignments 1, 2a, 2b) Part 2 – Activities on Examination day (Assignment 3): a ten-minute presentation based on the student's research, followed by a discussion.
The students will practice Academic vocabulary and grammar, as well as boost their reading, writing and speaking skills.
The blended format includes a weekly online workload plus an offline group tutorial providing a flexible and individualised learning trajectory.
Real-time feedback in online exercises will be complimented by tutor feedback for the writing and speaking assignments.
Flow assurance is an important discipline in petroleum engineering dealing with problems, which occur during oil production. Most of known flow assurance issues occur either in a production tubing or near wellbore area. The 1st part of the present course is focused on modeling of flow assurance processes. A number of problems will be considered. The major of them are as follows: – asphaltene deposition in a production tubing – wax deposition in a production tubing – reservoir impairment by asphaltenes – liquid-gas flows in a production tubing – emulsion formation and flow in a production tubing
Prevention of complication during production is mainly practical discipline answering to us: what complications are, which types of complications are expected in such conditions and how to prevent them? – WAX and asphaltenes. Deposition. Inhibitors. – Salts. Deposition. Inhibitors. – Corrosion. Inhibitors. – Emulsion. Demulsifier. – Oxygen and hydrogen sulfide absorbers. – Crude oil and water treatment.
Matematical methods of modern theory of Hamiltonian systems are based on the concepts, arosen in different fields of mathematics: differential equations and dynamical systems, Lie groups and algebras, differential geometry on manifolds. Many modern directions in mathematics (e.g. symplectic geometry) got their origin from the problems of classical mechanics.This course is recommended to all students, interested in mathematical physics, and it does not imply any special preliminary education in physics.
The preliminary program of the course includes: 1. Lagrangian formalism: minimal action principle, Euler-Lagrange equations, symmetries and integrals of motion, Noether theorem. 2. Simplest examples: dynamics for a single degree of freedom, Kepler's problem etc. 3. Basis of the Hamiltonian formalism: phase space, Legebdre transform, Hamilton equations, the Poisson and symplectic structures, Darboux theorem. 4. The Hamilton-Jacobi equation, canonical transform, Liouville theorem. 5. Integrable systems: separation of variables, Liouville integrability. Systems with Lax representation. 6. Examples of integrable systems: Toda and Calogero problems, integrable systems on Lie groups, geometry of spectral curves etc.
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.
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: – history of the subject; – main definitions in the AI field (and related confusions); – main areas of applications of AI; – current trends; – ethical aspects of AI and different approaches to the problem; – sustainable elopement of AI; – agendas of international organisation which are active in the area (ISO, UNESCO, IEE etc); – AI strategies of different countries; – social aspects of AI
As to the vicinity of the metal-insulator transition, I give a qualitative discussion of the mechanism behind the transition, as well as the most powerful tools for probing the properties of the system near the transition: analysis of inverse participation ratios and the concept of multifractality of the wave-functions.
In the metallic phase I discuss the weak localization corrections, including magnetoresistance, inelastic phase-breaking mechanisms and interaction-induced anomalies in the density of states near the Fermi surface.
At the end of the course I give a brief introduction to mesoscopics, including the Landauer formalism and quantization of the ballistic conductance.
As final project students should select one topic from the professors' lectures;find the professor's papers on this topic, read and analyze them (two-three most important/interesting ones)also, find some interesting papers from other groups, to get a contextwrite an essay / review (if you have ideas about what else could be done in the selected area, add them) – about 2-3 pages.
The final schedule (what lecture reads which professor, and when) will be available by the end of the September.
Tentative plan: linear Lie groups and their Lie algebras; universal enveloping algebras; Haar measure on a linear Lie group; general facts about representations of compact groups and their characters; radial part of Haar measure; Weyl's formula for characters of the unitary groups; Weyl's unitary trick; classification and realization of representations; symmetric functions.
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.
The course is addressed to undergraduates of the first year and contains applications of various mathematical methods for solving problems of mathematical physics. The course assumes a minor familiarity with various sections of theoretical physics (classical mechanics, field theory, quantum mechanics, statistical physics, hydrodynamics, elasticity theory) on the example of solving specific problems. The main purpose of the course is to encourage undergraduates to independent research work. For this reason, the main element of the course is an independent solution to the problem, requiring the study of additional material. In the endpoint the students are assumed to acquire the use of Green functions, distributions, Laplace and Fourier transforms, asymptotic evaluations in mathematical physics
Students activities include: -listening to lectures -discussions/seminars -homework -tests
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and require less than 6 hours of work. Your course instructor and Educational department should approve both assignments in Canvas in terms of content and formal requirements respectively.
Please note that those, who did not pass Pedagogy of Higher Education [PE03025] and are going to be TA in the current term, should register for another course Pedagogical Experience – Pre-requisite [PE03005pre].
The simplest approach is phenomenological and called thermodynamics, which treats macroscopic manifestations of hidden degrees of freedom and mean values. More sophisticated is statistical physics, which derives statistical laws, justifying thermodynamics and giving the probability of fluctuations. We start by briefly reminding basics of thermodynamics and stat-physics and the central role of entropy. We then describe how irreversible entropy growth can appear from reversible dynamics, learning the basics of dynamical chaos. We shall understand that the entropy is not the property of a system, but of our knowledge of the system and re-tell the story of statistical physics using the language of information theory, which shows its universality. We discuss numerous applications, from nano-particles to bacteria and from neurons to quantum computers. One magic instrument appears over and over: mutual information and its quantum sibling, entanglement entropy. I then describe the most sophisticated way to forget information – renormalization group, and modern generalizations of the second law of thermodynamics.
Overall goal is to give you the most powerful and universal tool developed in physics and applied everywhere, from computer science and machine learning to biophysics, economics and sociology. The course can be also of use for physicists illuminating subjects as diverse as black holes and nano-particles.
Course starts from the introduction of the main concepts: supersymmetric algebra, its representations, chiral and vector multiplets. One will consider Wess-Zumino model and introduce superpotential. Then one will consider supersymmetric quantum electrodynamics, discuss supersymmetric Higgs mechanism and Higgs branches. One will consider in detail supersymmetric quantum chromodynamics. Its vacuum structure at the classical level will be discussed. Then one will introduce instantons and explain how they change vacuum structure by generating Affleck-Dine-Seiberg superpotential. Finally we will consider Seiberg duality.
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.
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 technology 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 mechanical testing.
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.
The course assumes basic preliminary knowledge of quantum mechanics and statistical physics. Second-quantization formalism is introduced and used throughout the course.
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.
All Master and Ph.D. students within the Energy Program are encouraged to attend the Energy Colloquium during the entire period of their studies.
Students can earn 1 credit, if he/she participates in the Energy Colloquium over the course of any 2 terms of the academic year. Students who passed one round can make next (for credit) over the course of their subsequent studies.
Essential notions which are taught include: energy conversion; heat transfer; work; first and second principles; working fluids and thermoelastic coefficients; chemical reactions; thermodynamic cycles; motors and refrigerators: engines and heat pumps; sources of irreversibility; finite-time thermodynamics. Time permitting, notions of kinetic theory and statistical 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, and also perform some experimental activity in the Thermal Lab.
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.
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.
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
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.
-) Introduction to cryptography, type of ciphers. Private and Public crypto systems -) Hash functions.Digital signatures and certificates. Public key infrastructure -) Secret sharing, esoteric protocols, mental poker -) Introduction to data base systems. Distributed data base systems -) Main concept of blockchain. Consensus and Impossibility of Distributed consensus with One Faulty Process -) Network and computational assumptions. Consensus properties -) Atomic broadcast. Tendermint. Exonum -) Cryptocurrency, Certification, Anchoring. Industrial examples
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 two in-class projects, one homework and the final project.
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.
Upon completion of this course the students will be able to master:
1.Classes of Materials
crystalline solids ionic, covalent, metallic, polymers 3-D structures, polymorphism, importance of defects, effect of nanoscale phase diagrams, transformations, glasses, composites
2. Property of Materials
Electrical Optical Magnetic
3. Materials Chemistry Analysis Methods
Surface Sensitivity and Specificity X-ray Photoelectron Spectroscopy Ultraviolet Photoelectron Spectroscopy
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 2. Evolution and speciation 3. Systems biology 4. Reaction-diffusion systems and pattern formation 5. Networks 6. Game-theoretical models
For each class, there will be a paper that two people will present to the rest of the class. We will go down to the details of experiments – how things were done and what do the data/figures really show, so be prepared to answer in-depth questions. Presenters will start by stating the name of the paper/main authors and telling the take home message of the paper – why it is signficant, what problem it solved. Then they will proceed to the actual work. If there are methods/results mentioned in the paper that refer to prior work, you shall be prepared to answer questions about it too. The audience is supposed to read the paper being discussed beforehand and participate in discussions.
To pass, one would need to present a paper at least once during the module and actively take part in discussions of other papers. One absence is allowed no questions asked. Additional absences when unexplained will be a cause for no-pass grade. There will be a few home assignments. They must be submitted in time, typed–not written up–and done professionally (written in good language, be concise and free of spelling errors – consider them as part of academic writing exercises).
It is gonna be fun – students tend to like the seminar and its atmosphere
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.
Dear students, the optimum number of students on this course is 16, the maximum is 20.
Please note that the final list of participants will be selected by prof. Severinov after registration closes.
Please see the seminar webpage at http://crei.skoltech.ru/cee/education/wednesday-scientific-seminar/
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: a. For the 1st year masters: deep understanding of the given paper including reproduction of some numerical simulations b. For the 2nd year masters: literature review on the research topic including personal novel results (extended pre-defense format) c. For PhD: personal research results. PhD students may also provide additional questions to the feedback form and are encouraged to prepare online quiz on the topic.
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.
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.
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.
The course lays the foundation to undertake a robust analysis and design of opportunities for technological innovation. It helps PhD students to develop the ability to recognize, evaluate, and develop technological ideas into commercially viable product and service concepts, and build those concepts into viable business propositions.
We introduce tools and frameworks to help isolate and control the factors shaping the identification, evaluation and development of commercial opportunities. During the course, students first gain practical experience in shaping technology-based opportunities (originating from problems found in engineering and scientific education) and in identifying market-based opportunities (from social, economic and environmental contexts). Students are then challenged to employ that same commercialization framework to reflect on and examine ideas and scientific results from their own doctoral research, link these with appropriate market-based opportunities, and identify one or more pathways to create practical impact from their ideas.
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.
The course is designed primarily for the students: – having intention to join a startup or initiate their own tech focused venture; – interested in "corporate innovations" in hi-tech businesses.
As a framework for the course we use the “battle proven” approaches as “Disciplined Entrepreneurship” (Bill Aulet, MIT) and “Customer Development” (Steve Blank, UC Berkeley & Stanford). The course will have team- and project-based active learning formats with the combination of class lectures, “real-live” cases, in class and homework assignments (including “get out of the building and interview entrepreneur and/or customer” exercises!) as well as guest speakers’ sessions and teams’ activities and report outs. In the course you will be studying such topics as opportunity recognition and market evaluation, forming a startup team, start-up strategies, gathering resources, entrepreneurial marketing, revenue streams and pricing, as well as other you will need to know to increase your odds in putting a new technology to use and commercial success.
Special emphasis is given to provide you with networking opportunities and connections within Skolkovo innovation ecosystem.
The course serves as an introduction and prerequisite for the further in-depth E&I courses: Product Innovation (Term 3, new tech products & services design in cooperation with external business partners) and Technology Entrepreneurship: advanced (Term 4, deep-dive into your startup projects).
Also, so called Hardware-in-the-Loop (HiL) testing is important part of the course. The idea of HiL is to upload the functional model of 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.
Course structure: lectures, seminars, exam.
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.
• Introduction: What is a composite? Classification. Metals vs composites, advantages and disadvantages. Applications in industry. • Matrices. Micromechanics. • Mesomechanics: Stresses and strains. Ply. Laminate theories. Hygrothermal strains and stresses. Edge effect. • Manufacturing: Unidirectional vs. textile. Thermoplastic vs. thermoset. Prepreg vs. infusion. • Experimental characterisation: Tension, bending, compression, shear, impact, fatigue. • Damage and failure. Fracture toughness. • Finite element analysis. Abaqus.
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. The course includes practical experience of composite manufacturing and mechanical tests. During the last part of the course the participants will be presented a 'challenge' project in design and structural analysis, which they may attack experimentally, analytically or by means of finite-element package Abaqus. Participants are expected to demonstrate their collective knowledge while at the same time solving individually a real problem.
The main goal of this course is to represent the fundamental basis of different additive technologies 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 additive technologies on various printing machines. Students will be able to create their own models, print them in metals, ceramics and polymers, and also 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.
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.
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.
Older version: The present course could be also entitled 'Classical Field Theory', which menas it deals with all basic material needed in a study of fields preceeding to a study of their quantum properties. This requires in particular understanding such tools as Lagrangian, action functional, field equations (Euler-Lagrange equations). We shall also learn what are the most important symmetry principles which put certain constraints on a field theory. With this are related conservation laws. Typical important symmetries to mention are Lorentz and Poincare symmetry, conformal symmetry, gauge symmetry, general coordinate covariance. A traditional approach to Classical Field Theory has a perfect base in the 2nd volume of Landau-Lifshitz' course. However, since that prominent book was written, new elements came forward, which required more knowledge of differential geometry and topology. In our lecture course, we shall get familiar with most important basic facts from these branches of mathematics with application to field theory. For example, understanding instantons (even at a classical level) requires good knowledge of a number of notions from modern math courses, such as vector bundles, connections, homotopy groups. Therefore our course has to go beyond the reach of Landau-Lifshitz' volume 2.
The course is focused on the human immune system. The main medical aspects related to the functioning of the immune system will be considered, including autoimmune diseases, allergies, tumor-immune system interactions, immunotherapy, vaccinations and transplantation.
Particular attention will be paid to the adaptive branch of the immune system, immunogenomics and state-of-art approaches for immune repertoire studies, such as: application of the novel sequencing technologies and corresponding bioinformatic data analysis, novel approaches for the studying of the antibody and T-cell receptor repertoires in health and disease.
This course will be supplemented with lectures demonstrating data processing and statistical analysis of obtained results, as well as commonly used computational pipelines in R scripting language.
Intellectual property affects not only technology commercialization strategy but also the direction of scientific research itself. University research groups increasingly compete with each other for scientific reputation and access to resources on the basis of their ability to obtain patent protection for the practical applications of their research; but also on the basis of their ability to plot research pathways to maneuver around the "proprietary territory" of other research groups. Skill in using IP data bases, and associated analytical tools, can empower university scientific teams to craft more powerful research 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 classroom sessions will include lively discussions of case studies of the management of IP and the resolution of IP-related problems in the process of technology commercialization. Each student will conduct an analysis of intellectual property issues related to his or her own Ph.D. research topic. Use will be made of special IP data and IP analytics tools.
Intellectual property affects not only technology commercialization strategy but also the direction of scientific research itself. University research groups increasingly compete with each other for scientific reputation and access to resources on the basis of their ability to obtain patent protection for the practical applications of their research; but also on the basis of their ability to plot research pathways to maneuver around the “proprietary territory” of other research groups. Skill in using IP data bases, and associated analytical tools, can empower university scientific teams to craft more powerful research strategies.
To make the material self-contained, we also address the concept of tensorization, which allows for the creation of very high-order tensors from lower-order structured datasets represented by vectors or matrices. Then, in order to combat the curse of dimensionality and possibly obtain linear or even sub-linear complexity of storage and computation, we address super-compression of tensor data through low-rank tensor decompositions. We also explain the concepts of low-rank matrix/tensor approximations and the associated machine learning algorithms. We then elucidate how these concepts can be used to convert otherwise intractable huge-scale optimization problems into a set of much smaller linked and/or distributed sub-problems of affordable size and complexity. In doing so, we highlight the ability of tensor decompositions to account for the couplings between the multiple variables, and for multimodal, incomplete and noisy data.
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; solar energy conversion.
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.
The course is intended to provide the understanding and working knowledge of numerical methods required for modeling and simulation of complex phenomena. It focuses on fundamentals of the methods such as accuracy, stability, convergence, and consistency rather than learning how to use canned computer codes. The course involves first-hand experience with programming and solving real problems on computers. Solid knowledge of calculus, linear algebra, complex variables is essential, and basic understanding of the theory of ordinary and partial differential equations of physics and engineering as well as basic programming skills are required. The following topics will be covered: solving nonlinear equations, matrix equations, eigenvalue problems, interpolation, numerical differentiation and integration, numerical solution of ordinary and partial differential equations, optimization problems, and data analysis.
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 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.
Within this hands-on immersive course you are to run a team-based projects. This will enable you to put into practice the course tools and techniques as well as to create your own “product innovation” professional portfolio. The projects will be done in collaboration with real startups/ventures facing the challenges of a new product design. (If you have your own idea/startup you would like to run as the course project, please contact instructor).
In this course we will balance key concepts and methods of generating and testing customer-centric products and business ideas (e.g. Customer Development & Lean Startup, Design Thinking, Business Model Innovation) with the "deep dive" into prospective technologies and markets in the areas of Internet of Things, Smart & Connected Devices, Digital, New Materials, Manufacturing Technologies, Industry 4.0.
Protein structure: Amino acids. Main elements of secondary structure. Tertiary structure. Quaternary structure. Intrinsically disordered proteins.
Protein functioning: Enzymes: general principles of enzymatic catalysis and its regulation. Structural proteins. Signaling cascades.
Cofactors: Redox-active cofactors. Light-sensitive cofactors. Cofactors for chemical transfer. Energy-driving cofactors.
Posttranslational modifications: Main types of PTM (glycosylation, lipidation, phosphorylation, methylation, acetylation, PARylation, proteolysis, crosslinking). Some rare types of PTM.
Intracellular protein trafficking: Synthesis of membrane proteins. Export to ER, Golgi and extracellular space. Exocytosis, endocytosis. Nucleus/cytoplasm exchange: nuclear pore. Import to mitochondria. Import to peroxisomes. Membrane-penetrating peptides.
Protein degradation: Proteosomal degradation. Lysosomal degradation.
Protein engineering: Site-directed mutagenesis. Epistatic substitutions. Random mutagenesis, directed molecular evolution. Protein labeling. Protein split and permutation. Domain exchange. Protein localization signals. Biochemical optogenetics.
Starting with the theory of ground state quantum computation, the course builds the tools needed to understand modern day quantum simulation algorithms, as executed on quantum computers.
The topics covered include:
Penalty functions and embedding problems into the ground state of Ising spins Quantum vs stochastic models of computation Hamiltonian simulation and variational algorithms Quantum enhanced machine learning
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.
Here is the list of topics which will be discussed in the course.
– Coordinate Bethe ansatz on the example of the Heisenberg model and one-dimensional Boe gas with point-like interaction between particles.
– Bethe ansatz in exactly solvable models of statistical mechanics on the lattice.
– Calculation of physical quantities in integrable models in thermodynamic limit, thermodynamic Bethe ansatz.
– Bethe equations and the Yang-Yang function, caclulation of norms of Bethe vectors.
– Quantum inverse scattering method and algebraic Bethe ansatz, quantum R-matrices, transfer matrices, Yang-Baxter equation.
– Functional Bethe ansatz and the method of Baxter's Q-operators, functional relations for transfer matrices, transfer matrices as classical tau-functions.
The knowledge of quantum mechanics and statistical physics for understanding of the course is highly desirable but not absolutely necessary. Out of the physical context ansatz Bethe in its finite-dimensional version is simply a method for diagonalization of big matrices of a special form. In this sense it does not require anything except the basic notions of linear algebra.
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.
The goal of this course is to give a broad review of modern photonic and 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 theory of electromagnetic response of quantum systems, centered around linear response functions and their spectral properties, is outlined. The rest of the course describes such quantum materials as graphene and graphene-based structures, topological insulators, topological Dirac and Weyl semimetals, high-temperature superconductors, exotic magnetics, transition metal dichalcogenides, oxide interfaces, and novel electromagnetically-engineered quantum materials. The students will be introduced to basic models and to both conventional and ultrafast pump-probe spectroscopy studies of these materials.
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.
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 on which important in the field papers will be discussed.
After the accomplishment of the course, students will know the history of virology, its current problems as well as directions for further development.
During the course, students should develop the technology for small unmanned aerial vehicle (UAV) prototype production. Educational process includes manufacturing technology development, prototype production, prototype testing, model validation. During the course, students learn how to use special software Simcenter 3D, LMS Test.Lab, LabView, 3D printers’ software, test machines
The course provides students with a theoretical and practical basis for advanced manufacturing of complex systems, such as UAV and forms the final understanding of the product lifecycle management.
The course is offered primarily for CDISE students who are assumed to know the basics of image processing and machine learning. DS and IST students get 3 credits for completing this course. 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 prerequisites are: undergraduate math, chemistry, and physics.
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 data plays an important role in modern learning theory and data analytics. Real-world data obtained from natural sources are usually non- uniform and concentrate along lower dimensional structures, and geometrical methods allow discovering the shape of these structures from given data.
Originally being part of dimensionality reduction research, geometrical methods in machine learning has now become the central methodology for uncovering the semantics of information from the data.
The aim of the course is to explain basic ideas and results in using the modern geometrical methods for solving main machine learning problems such as classification, regression, dimensionality reduction, representation learning, clustering, etc.
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. Necessary short information on differential geometry and topology will be given in the course.
The course lets students to be involved in meaningful real-life machine learning projects, such as mobile robot navigation, neuroimaging, to cope with challenging problems.
During this course students will be introduced with open problems in chemoinformatics and with state-of-the-art machine learning methods attempt to solve these problems. Particularly students will practice deep learning, including 3D convolutional neural networks and generative adversarial networks, for drug discovery and molecular design problems. The course includes three theoretical lectures, that cover basics of 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 final project aimed to solve chemoinformatics problem of choice using machine learning.
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. 2. Composites which combine reinforcing components of two scales (microscopic fibers and nano-reinforcement) and are known as hierarchical composite materials or nano-engineered fibre reinforced composites (nFRC). These materials have a complex structure with several levels of organization and distinguishable macroscopic, mesoscopic, microscopic, and nanoscopic features.
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.
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. We will select several state-of-the-art papers, each focused on the analysis of a particular ‘omics data type, and guide students through the data analysis steps in the paper, allowing them to reproduce the key methodological procedures. In doing so, 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 chapters of the course will include: Genotyping Genome-wide association studies Genome assembly Transcriptome assembly Differential expression analysis Alternative splicing analysis Single-cell expression analysis ChIP-seq and ATAC-seq data analysis Hi-C data analysis Lipidome and metabolome data analysis
The course will end with the task (Final Project) on the integration of different data types produced to answer the same biological question. Such integration analysis is at the cutting of bioinformatics and will be of great use to SkolTech students.
Physical principles of seismic propagation and mathematical principles of signal processing will be reviewed. All processing steps will be thoroughly covered: starting from raw seismic data visualization to the construction of a seismic image of the subsurface.
Application of these techniques will be performed on synthetic and real data.
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.
This is an introductory NLP course dedicated to classic algorithms based on the Jurafsky and Martin textbook. Later, we will offer also an additional course covering neural methods for NLP based on the Goldberg textbook.
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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 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.
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.
"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
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.