Research Groups (Center for Artificial Intelligence Technology)

Computational Intelligence, Prof. Ivan Oseledets 

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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.

  • Robustness of deep learning models: how to build adversarial attacks, how to detect adversarial attacks and how to train more robust and better certified models in real-world scenarios.Tensor methods in ML: the use of tensor approximations in building new classifiers and density estimators, especially for tabular data.
  • Non-euclidean geometry and deep learning: the usage of hyperbolic spaces to improve over state-of-the art models in deep learning
  • Improving explainability in DL models: how to enforce additional constrains on the hidden representations to make the resulting model less unpredictable
  • Recommender systems: building new efficient approaches for recommender systems
  • Physics-informed machine learning: development of efficient solvers for equations of mathematical physics based on machine learning
  • Different applied CV/NLP projects in different domains, including agriculture, design and recommender systems.

Courses delivered in Skoltech: Numerical Linear Algebra, Deep Learning, Research Methodology, Introduction to Recommender Systems.

Mobile RoboticsProf. Gonzalo Ferrer

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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.


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  • Mapping and Localization with Tensor based TSDF
  • Neural Matchers for Visual Localization
  • Deep Learning Depth Prediction for SLAM
  • Human Gesture Detection and Motion Prediction
  • 3D Mapping with Colored Point Clouds

Courses delivered in Skoltech: Planning Algorithms in Artificial Intelligence, Perception in Robotics.



Natural Language Processing, Prof. Alexander Panchenko

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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.

  • Lexical semantics (word sense induction and disambiguation, frame induction and disambiguation, semantic similarity and relatedness, sense embeddings, automated construction and completion of lexical resources such as WordNet and FrameNet)
  • Argument mining (comparative argument mining, and argument retrieval)
  • Learning representations of linguistic symbolic structures (graphs) such as knowledge bases and lexical resource
  • NLP for a better society: recognition of fake news, hate speech, and related phenomena
  • Textual style transfer
  • Tensors for NLP: learning representations based on tensor decompositions and tensor networks

Courses delivered in Skoltech: Introduction to Natural Language Processing, Deep Learning for Natural Language Processing.


Intelligent Signal and Image ProcessingProf. Anh Huy Phan

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The group research interests include multilinear algebra, tensor computation, tensor networks, nonlinear system, blind source separation, and brain–computer interface.

  • Tensor structure for Light Convolution Neural Networks
  • Efficient architectures for GAN compression
  • Parallel estimation method for tensor decomposition based on rank-1 tensor approximation
  • Tensor network for intelligent image analysis
  • Tensor Networks for Machine Learning
  • Deep Learning Methods for Brain Computer Interface

Courses delivered in Skoltech: Convex Optimization and Applications, Tensor Decompositions and Tensor Networks in Artificial Intelligence.


Multiscale Neurodynamics for Intelligent SystemsProf. Jun Wang


  • Sparse Bayesian learning via neurodynamic optimization and its applications to nonlinear system identification, time series forecasting, stock index tracking etc
  • Neurodynamics-driven throughput optimization for  simultaneous wireless information and power transfer in wireless sensor networks
  • Neurodynamics-driven Unit commitment and economic dispatch in electric power grids
  • Constrained supervised learning in neural ordinary differential equations via collaborative neurodynamic optimization
  • Neurodynamics-driven portfolio selection with various performance criteria and constraints


Mathematical Foundations of AIProf. Dmitry Yarotsky

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The research group has wide range of interests including theoretical and applied machine learning, approximation theory, optimization and mathematical physics.

  • Theoretical Results in Deep Learning
  • Machine Learning in Music Analysis

Courses delivered in Skoltech: Theoretical Methods of Deep Learning, Scientific Computing.


AI & Supercomputing, Prof. Sergey Rykovanov

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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.

  • Development of the radiation calculation toolkit on GPUs
  • High Performance Computing for condensation driven aggregation problems
  • Virtualization of scientific workflow on supercomputers
  • Development of a High Performance Particle-In-Cell code for Plasma physics simulations
  • Virtualization and containerization in modern HPC environments (Kubernetes/Docker/Singularity)

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 optimisationProf. Vladimir Palyulin

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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.​

  • Anomalous diffusion challenge: finding the best algorithm to classify trajectories with machine learning
  • Reinforcement learning for target search optimisation
  • ML for traffic optimisation problems
  • Non-backtracking random walks on multiplex networks
  • Optimisation of escape times by RL approaches

Courses delivered in Skoltech: Stochastic Methods in Mathematical Modeling, Soft Condensed Matter, Thermodynamics and Transport at Nanoscale.


Computational Imaging, Prof. Dmitry Dylov

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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.

  • Computer vision. A new type of data augmentation for face detection. Unsupervised learning. Training frequency spectra instead of pixel neural networks. A series of successful projects on the application of algorithms in oncology and cardiology.
  • Visualization systems. Portable MRI device (ongoing). Speeding up clinical MRI setups 16 times. Super-resolution microscopy from a regular camera. Visualization device, autofocus, and detection of objects in the dark and under adverse weather conditions.
  • Nonlinear method of transmitting energy to signal (image) from noise. The theory of optimal transfer in reinforcement imitation learning, etc.

Courses delivered in Skoltech: Computational Imaging, Biomedical Imaging and Analytics.


AI for Materials DesignProf. Alexander Shapeev

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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.

  • Machine-Learning Interatomic Potentials
  • Machine Learning for Elastic Strain Engineering
  • Atomistic-to-Continuum Coupling

Courses delivered in Skoltech: Numerical Modeling, Advanced Materials Modeling.


AI-driven Modeling, Prof. Ekaterina Muravleva

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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

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