Master of Science Program

Skoltech CDISE

Modern science and engineering critically rely on efficient and fast computational techniques and models. ACS program achieves the synergy of state-of-the-art mathematical modeling methods (numerical ODE and PDE, stochastic modeling, machine learning and Big data-based approaches) and their implementation with modern high performance parallel computational facilities furnished with up-to-date software. The cutting-edge scientific MSc project solidifies the theoretical knowledge obtained in the courses.

Aim and objectives

Track
Data-Intensive Mathematical Modelling and Simulations (DIMMS)

This track aims at fostering a new generation of computational scientists and engineers, able to combine first principle and data-driven approaches in mathematical modeling of natural, industrial and social phenomena. The curriculum carefully balances advanced computing, machine learning, and computational physics to implement large scale models in modern computational environments.

 A successful graduate of this track will be able to:

• Construct mathematical models of industrial processes, natural, and social phenomena based on fundamental principles and available data
• Contribute to the development of efficient algorithms and codes for computationally demanding, data-intensive modeling and simulations
• Apply relevant computational approaches, data structures, hardware, and software to complex real-world problems.

Track
High Performance Computing (HPC) and Big Data

The modern computational world is essentially parallel as CPUs and GPUs contain multiple cores. Datasets and computational problems are becoming impossible to be processed using a single compute node.

Besides pursuing an academic career, HPC track students with knowledge of modern computing architectures, programming, code optimization, and distributed deep learning will easily find Data Scientist, Software Engineer, or IT-specialist positions in various industries, including IT, Oil & Gas, Finance & Banking, Industrial R&D, Manufacturing and more.

A successful graduate of this track will be able to:

• Effectively address modern computing world challenges using existing and state-of-the-art HPC and Big Data frameworks in a variety of applications (deep learning, data analytics, mathematical modeling of complex events)
• Solve mathematical modeling and data-intensive tasks using parallel computing
• Develop and optimize massively parallel computer codes
• Create efficient infrastructures for HPC clusters, Big Data, and Data Centers

MSc Program Structure

Download curriculum

Career opportunities and paths

• Industry
• Landing specialist positions such as Data Analyst, Data Scientist, Industrial Research Scientist, Consultant in various industry sectors (Сhemical and Pharmaceutical industry, Oil & Gas, IT, Finance, and others).
• Science
• Landing PhD positions and continuing research at leading Russian and international research bodies.
• Startup
• Starting a business on their own or through the Skolkovo innovation ecosystem with its extensive pool of experts, consultants and investor

Faculty

Program Director Nikolai Brilliantov Full Professor

Program coordinator Vladimir Palyulin Assistant Professor

• Evgeny Nikolaev, Full Professor
• Ivan Oseledets, Full Professor
• Sergey Rykovanov, Associate Professor
• Evgeny Burnaev, Associate Professor
• Alexey Vishnyakov, Associate Professor
• Dmitry Yarotsky, Associate Professor
• Alexander Shapeev, Associate Professor
• Mikhail Belyaev, Assistant Professor
• Nikolay Koshev, Assistant Professor
• Petr Popov, Assistant Professor
• Dmitry Yudin, Assistant Professor
• Natallia Strushkevich, Assistant Professor
• Yury Kostyukevich, Assistant Professor
• Gregory Kucherov, Leading Research Scientist
• Igor Zacharov, Senior Research Scientist
• Alexey Artemov, Research Scientist
• Sergey Matveev, Junior Research Scientist

Research
Students are actively involved into research activity starting from Term 3.

Main research areas:

• Mathematical and Supercomputer Modelling
• Big Data and distributed deep learning
• Modern Computing architectures and technologies
• Efficient Numerical Algorithms
• Soft Matter and stochastic processes
• Physics for machine learning and machine learning for physics
• Physics for social sciences
• Mathematical modeling of large-scale complex phenomena (plasmas, multi-component, and multi-phase fluids and gases)

• Drug design and computational design of new pharmaceuticals
• Reinforcement learning for target search, flock formations
• Distributed graph analytics on modern supercomputing architectures
• Modeling of geomechanics for the oil industry
• Femtosecond optics
• Large-scale molecular modeling and optimization of properties of new chemicals

Research groups:

• High-Performance Computing (HPC) (Prof. Sergey Rykovanov)
• Advanced Multiscale Simulations Lab (Prof. Nikolay Brilliantov, Prof. Alexey Vishnyakov, and Prof. Vladimir Palyulin)
• Inverse and ill-posed problems of mathematical physics (Prof. Nikolay Koshev)
• Computational Intelligence (Prof. Ivan Oseledets)
• Mass Spectrometry Lab (Prof. Evgeny Nikolaev, Prof. Yury Kostyukevich)
• Cognitive Computing Hardware (CONCORDE), (Prof. Dmitry Yudin)

Academic Partners:
Moscow Institute of Physics and Technology, Russia

• Keldysh Institute of Applied Mathematics, Russia
• National Research Center “Kurchatov Institute”, Russia
• Tomsk State University of Control Systems and Radioelectronics (TUSUR), Russia
• Higher School of Economics, Russia
• ETH Zürich, Switzerland
• Nokia Bell Labs, UK
• MIT, USA

Industrial partners:

• Severstal
• GazpromNeft
• NVidia
• Bruker
• BioCAD
• Chemrar
• Insilico Medicine
• КРОК
• Niagara