PhD research topics

CDISE faculty welcome applicants to undertake a PhD project in one of the areas of their research interest, as listed below. Bear in mind that it is imperative to contact a prospective research supervisor (or several) from CDISE faculty members prior to your application.

For more details about available PhD projects, visit open PhD positions or reach out to corresponding research supervisors.

CDISE faculty members Research topics
Professor of Practice
Alexander Bernstein
  • Mathematical modeling
  • Mathematical statistics
  • Intelligent data analysis
  • Geometrical and statistical methods in data analysis
  • Manifold Learning
  • Machine Learning
  • Analysis of neuroimaging and biomedical data
Visiting Professor
Christoph Borchers
  • Improvement, Development, and Application of Proteomics and Metabolomics Quantitative Technologies for Clinical Diagnostics
  • Development of Precision Medicine Based on Coupling of Big Data and Multi-Omics Studies
  • Correlating Patterns in Proteogenomics Data to Improve Precision Oncology
  • Integrating Structural Proteomics Experimental Mass Spectrometry Data for Solving Protein Structures for Biomedical Research
Nikolay Brilliantov
  • Polyelectrolyte-based Nano-actuators, Nano-tribology
  • Mathematical Modeling of Complex Fluids, Polyelectrolyte, and Colloidal Solutions, and their biological applications
  • Mathematical Modeling of Granular Matter, Granular Hydrodynamics and Kinetics
  • Mathematical Modeling of Astrophysical Systems
  • Theory of far-from-equilibrium processes and pattern formation
  • Mathematical Modeling of systems of active particles and traffic modeling
Associate Professor
Evgeny Burnaev
  • Generative modeling
  • Manifold learning
  • Deep learning for approximation of physical models
  • Deep learning for 3D computer vision and neurovisualization
Andrzej Cichocki
  • Application of AI for early diagnosis and detection of some mental diseases
  • Tensor Networks and Tensor Decomposition for Machine Learning and Deep Learning
  • EEG Brain-Computer Interfaces, Human-Computer Interactions, and Human Cooperation
  • Signal/Image Processing and Machine Learning Algorithms
  • Portfolio Optimization and Time Series Analysis
  • Time Series Forecasting Using Deep Neural Networks and Machine Learning
  • Humanoid Robotics and Human-Robot Interactions
Associate Professor
Dmitry Dylov
  • Computational imaging
  • Computer Vision
  • Medical Vision
  • Fundamental Aspects of Imaging
Assistant Professor
Gonzalo Ferrer
  • Robotics
  • Path Planning
  • Perception
  • Human Motion Prediction
  • Dynamic Environments
  • Localization
  • Mapping
  • Sensor Fusion
Associate Professor
Alexey Frolov
  • Information theory for deep learning
  • Machine learning in communications
  • Coded Distributed Computing
  • LDPC and Polar codes and their applications to future 5G wireless networks
  • Non-orthogonal multiple access (NOMA) schemes for massive Internet of Things
  • Random access protocols (coded slotted ALOHA)
  • Coding for distributed and cloud storage systems
  • Coding for fiber optic lines
  • Post-quantum (code and lattice-based) cryptography
Assistant Professor
Nikolay Koshev
  • Inverse and ill-posed problems of mathematical physics
  • Differential equations
  • Integral equations
  • Mathematical modeling
  • Magneto- and electroencephalography (MEG/EEG)
  • Microscopy
  • Tomography
  • Signal and Image processing
Assistant Professor
Yury Kostyukevich
  • High-resolution mass spectrometry
  • Analysis of complex natural mixtures, proteomics, metabolomics
  • Gas-phase ion chemistry
  • Instrumentation development and supercomputer simulation of ion optic
Associate Professor of Practice
Dmitry Lakontsev
  • Wireless Technologies
  • Internet of Things
  • Telecommunication systems
Associate Professor
Victor Lempitsky
  • Computer vision
  • Visual recognition
  • Biomedical image analysis
Evgeny Nikolaev
  • Supercomputer modeling of ion clouds behavior in accumulation and transport mass spectrometer devices
  • Further development of Particle in Cell Algorithm and Code for FT ICR signals simulation
  • Development of analytical solution for the dynamically harmonized FT ICR cell
  • Quantitative mass spectrometry for personalized medicine
  • Investigation of microgravity influence on astronaut’s body liquid proteome and metabolome by quantitative mass spectrometry
  • Omics technologies
  • Development and application of on fly H/D exchange methods
  • Classification analysis of organic carbon natural storages using ultrahigh accuracy mass spectrometry (Fourier Transform Ion Cyclotron Resonance Mass Spectrometry)
Ivan Oseledets
  • Solution of multidimensional integral and differential equations discretized on fine grids
  • Ab initio computations in quantum chemistry and computational material design
  • Construction of reduced-order models for multiparametric systems in engineering
  • Uncertainty quantification in engineering sciences
  • Data mining and compression
Assistant Professor
Pavel Osinenko
Two major directions:

  • Reinforcement learning (RL)
  • Safe AI

Topics in RL:

  • Deep RL and its convergence
  • Stability and safety of RL
  • Predictive RL
  • Fusion of image recognition and RL
  • Applications of RL: robots (wheeled, legged, manipulators), economic optimization agriculture, etc.

Topic in safe AI:

  • Adversarial robustness, in particular via control theory and Lyapunov methods
  • Verified computation and formal verification
  • Stability and safety of dynamical AI systems
Assistant Professor
Vladimir Palyulin
  • Stochastic processes and phenomena
  • Target search optimization
  • Machine learning applications in statistical physics
  • Reinforcement learning for search optimization
  • Mathematical modeling of soft matter
  • Mathematical modeling of traffic problems
Assistant Professor
Alexander Panchenko
  • Lexical semantics (especially 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 (especially 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
Assistant Professor
Maxim Panov
  • Bayesian methods in machine learning and statistics
  • Uncertainty estimation for machine learning models
  • Algorithms and statistical analysis of complex networks
  • Gaussian processes regression
  • Statistical inference, semiparametric inference
Associate Professor
Anh-Huy Phan
  • Tensor decomposition and tensor networks for ML applications
  • Deep convolutional tensor network
  • Intelligent signal processing
  • Tensor fusion network and multimodality analysis
  • Blind Sources Separation
  • Brain-Computer Interface
Assistant Professor
Petr Popov
Deep learning in structural bioinformatics and chemoinformatics
Associate Professor
Sergey Rykovanov
High-performance computing, computational physics
Assistant Professor
Andrey Somov
  • Intelligent sensing and data analysis in the scope of eSports, biomedical, and precision agriculture applications.
  • Power management, energy harvesting for wireless sensor networks, and wearables.
Vladimir Spokoiny
  • Image analysis and its applications to medicine,
  • Adaptive nonparametric smoothing and hypothesis testing,
  • High dimensional data analysis,
  • Statistical methods in finance and nonlinear nonstationary time series
Assistant Professor
Natallia Strushkevich
Applications of computational and machine learning methods for design and development of:

  • protein-protein interaction (PPI) inhibitors (applicable in breast and prostate cancers) and activators (Alzheimer disease);
  • novel scaffolds for inhibition of evaluated target enzymes (infectious diseases);
  • prediction of drug metabolism by cytochrome P450 enzymes.
Associate Professor
Alexey Vishnyakov
  • Advancing of mathematical modeling methodology & simulation techniques
  • Physics-informed machine learning
  • Porous structures & interfaces: characterization, thermodynamics, and transport
  • Structure-property relationships in soft matter
  • Statistical mechanics, especially in application to complex media and molecules
Associate Professor
Dmitry Yarotsky
  • Theoretical methods of deep learning
  • Applications of machine learning to wireless communication and bioinformatics
Assistant Professor
Dmitry Yudin
  • Neuromorphic computing with a focus on both algorithm development and hardware
  • Variational quantum algorithms and quantum computing
  • Computational materials science and atomistic-scale modeling
Assistant Professor
Alexey Zaytsev
  • Advanced algorithms for Bayesian optimization
  • Embeddings for weakly structured data
  • Adversarial attacks for categorical data
  • Anomaly detection approaches
Adjunct Professor
Denis Zorin
  • Theory and practical algorithms for subdivision surfaces, surface deformation, and mapping
  • Efficient computational methods for integral equations
  • Geometric modeling
  • Geometry processing
  • Scientific computing