Computational Intelligence, Prof. Ivan Oseledets
Google Scholar, I.Oseledets@skoltech.ru.
Research focuses on the development breakthrough numerical techniques (matrix and tensor methods) for solving a broad range of high-dimensional problems and their combination with deep learning in order to reduce computational complexity and improve robustness of such algorithms.
Courses delivered in Skoltech: Numerical Linear Algebra, Deep Learning, Research Methodology, Introduction to Recommender Systems.
Mobile Robotics, Prof. Gonzalo Ferrer
Google Scholar, G.Ferrer@skoltech.ru.
Research focuses on the autonomy for mobile robots, that is, being able to deploy robots in indoors, urban or outdoors environments and robots being able to execute any given task efficiently, reliably and safely. To achieve this, it is important to investigate on Perception (how to perceive and understand the world) and Action (how to actuate in succeed in a given task) and the combination of both topics into new solutions.
Courses delivered in Skoltech: Planning Algorithms in Artificial Intelligence, Perception in Robotics.
Natural Language Processing, Prof. Alexander Panchenko
Google Scholar, A.Panchenko@skoltech.ru.
Main research interest is computational lexical semantics, including word sense embeddings, word sense induction, extraction of lexical resources, and other related topics. Research group members are interested in argument mining, neural and statistical natural language processing, information retrieval, knowledge bases, machine learning and intersections/interactions of these fields.
Courses delivered in Skoltech: Introduction to Natural Language Processing, Deep Learning for Natural Language Processing.
Intelligent Signal and Image Processing, Prof. Anh Huy Phan
Google Scholar, A.Phan@skoltech.ru.
The group research interests include multilinear algebra, tensor computation, tensor networks, nonlinear system, blind source separation, and brain–computer interface.
Courses delivered in Skoltech: Convex Optimization and Applications, Tensor Decompositions and Tensor Networks in Artificial Intelligence.
Multiscale Neurodynamics for Intelligent Systems, Prof. Jun Wang
Mathematical Foundations of AI, Prof. Dmitry Yarotsky
Google Scholar, D.Yarotsky@skoltech.ru.
The research group has wide range of interests including theoretical and applied machine learning, approximation theory, optimization and mathematical physics.
Courses delivered in Skoltech: Theoretical Methods of Deep Learning, Scientific Computing.
AI & Supercomputing, Prof. Sergey Rykovanov
Google Scholar, S.Rykovanov@skoltech.ru.
In Skoltech Sergey and his team develop a portfolio of HPC related educational courses and perform state-of-the-art research in HPC and its applications. Sergey has strong competence in HPC, especially in massively parallel computational plasma physics, and in design of novel particle accelerators and X-ray sources.
Courses delivered in Skoltech: High Performance Computing and Modern Architectures, High Performance Python Lab, Parallel Computing in Mathematical Modeling and Data-Intensive Applications.
Quantum algorithms for machine learning and optimisation, Prof. Vladimir Palyulin
Google Scholar, V.Palyulin@skoltech.ru.
The group current research interests include various fields of statistical physics and stochastic processes research such as trajectory analysis, reinforcement learning for target search, anomalous diffusion and crowding problems.
Courses delivered in Skoltech: Stochastic Methods in Mathematical Modeling, Soft Condensed Matter, Thermodynamics and Transport at Nanoscale.
Computational Imaging, Prof. Dmitry Dylov
Google Scholar, D.Dylov@skoltech.ru.
Research focuses mainly on the fundamental optics principals analytics to be interpreted and implemented along with the modern computer vision methods for wide range of tasks in imaging.
Courses delivered in Skoltech: Computational Imaging, Biomedical Imaging and Analytics.
AI for Materials Design, Prof. Alexander Shapeev
Google Scholar, A.Shapeev@skoltech.ru.
Modern industrial products are designed on a computer. However, modern materials science algorithms are not efficient for optimizing many materials properties of interest, such as alloy hardness or Li-ion battery power. This research group is working on applying machine learning (ML) to revolutionize computational materials algorithms and molecular modeling, just like ML revolutionized technologies such as computer vision.
Courses delivered in Skoltech: Numerical Modeling, Advanced Materials Modeling.
AI-driven Modeling, Prof. Ekaterina Muravleva
Google Scholar, E.Muravleva@skoltech.ru.
Courses delivered in Skoltech: Introduction to Data Science.
Parallel algorithms for AI, Prof. Alexander Mikhalev
Courses delivered in Skoltech: Foundations of Software Engineering.
Tensor Networks & Deep Learning, Prof. Andrzej Cichocki
Google Scholar, A.Cichocki@skoltech.ru.