Find Academic Supervisors

• computer security
• compiler technologies
• parallel programming
• +2 more

Padaryan Vartan Andronikovich serves as an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE University) since 2022. He concurrently holds positions at the Basic Department "System Programming" of the Institute for System Programming named after V.P. Ivannikov of the Russian Academy of Sciences (ISP RAS), as Associate Professor at Moscow State University's Faculty of Computational Mathematics and Cybernetics since 2010, and as Head of the Laboratory of Reverse Engineering of Binary Code at ISP RAS since 1999. Since 2018, he has chaired Subcommittee 4 "Secure Software Development" of Technical Committee for Standardization 362 "Information Security". Education: 2005: Candidate of Physical and Mathematical Sciences 2000: Specialty in "Applied Mathematics" from Moscow State University, qualification "Mathematician. Systems Programmer" Dr. Padaryan's research centers on binary code analysis and security applications. His expertise spans computer security, compiler technologies, parallel programming, binary code analysis, and emulation and virtualization. His work addresses critical challenges in understanding and securing software systems when source code is unavailable, with particular focus on reverse engineering techniques, vulnerability detection, and secure software development methodologies. His research bridges theoretical computer science with practical security applications, contributing to innovative tools for binary code analysis. His publication record reveals a strong trajectory in binary code analysis, with consistent contributions to algorithm extraction, intermediate representation recovery, and security vulnerability detection. The interdisciplinary nature of his work combines programming languages, software engineering, and cybersecurity to develop practical solutions for analyzing and securing software systems. His research has evolved to address increasingly complex security challenges, particularly in network interface testing and data leakage detection in binary environments. Dr. Padaryan actively contributes to academic and professional communities through teaching and leadership roles. He teaches "Computer Architecture and Assembly Language" for Software Engineering bachelor's students at HSE University across multiple academic years. His laboratory leadership at ISP RAS has fostered significant research output, while his standardization committee role demonstrates influence in shaping secure software development practices in Russia. His laboratory work focuses on developing advanced security tools including the "LAKMUS" system for labeled data analysis and the "PROTOSPHERE" system for network traffic analysis. These tools represent practical applications of his research in real-world security scenarios, enabling security professionals to detect vulnerabilities and analyze software behavior without access to source code.

• Mathematical models identification
• Signal and image processing
• Digital holography
• +7 more

Igor Gurov is a Professor at the Faculty of Applied Optics at ITMO University, with a 32-year career in optical-electronic systems, digital signal processing, and computer image analysis. His roles include project leadership in advanced optical diagnostics and interdisciplinary research spanning metrology, tomography, and intelligent recognition technologies. Academic affiliation: ITMO University Research focus: Mathematical modeling, signal/image formation, and processing Leadership roles: Department Head (2005-2012), Project Leader in multiple innovations Research Interests: Professor Gurov specializes in mathematical models identification for optical systems, particularly in interferometry , digital holography , and optical tomography . His work includes multidimensional optimal filtering , stochastic dynamic systems , and 3D image formation in information systems. Research Trends: Recent publications demonstrate a focus on nonlinear Kalman filtering in optical coherence tomography, recurrence algorithms for fringe pattern analysis, and white-light microscopy innovations. Key themes include dynamic signal processing , multi-body motion estimation , and minimum description length principle applications. Projects: From 2006-2013, he led developments in telemedicine diagnostics , biotissue evaluation , and intelligent recognition algorithms , with technical expertise in high-performance video applications and 3D image representation. Contact: Email: gurov@mail.ifmo.ru

• Economics
• Finance
• Higher Education Administration
• +5 more

Andrey Ivanov is an Associate Professor and Head of the Department for Economics and Finance at ITMO University. With a 50-year career at the university, he transitioned from technical research to leadership roles in economics, finance, and higher education administration. He holds a PhD and earned the title of Senior Research Associate in 1990. His research spans Optical Systems Design (1970s–1980s) and Strategic Development in Higher Education , including contributions to ITMO’s innovation programs (2007–2009). Recent work focuses on Student Administration and Financial Leadership . His publications (see list below) highlight early work in Automation and Control and Optical Engineering . Awards include state medals and ministerial honors for his service to education and science. Jubilee Medal “300 Years of the Russian Navy” Medal “In memory of the 300th anniversary of St. Petersburg” Badge “Honorary Worker of Higher Professional Education of the Russian Federation” Medal “200 Years of the Ministry of Defense” He has participated in over 80 national and international research projects, holding leadership roles in ITMO’s educational reforms and strategic planning.

• Automatic machine learning algorithms
• Optimization methods
• Clustering algorithms
• +2 more

Sergey Muravyov is an Assistant Professor at the Institute of Applied Computer Science, ITMO University. He holds a PhD in theoretical foundations of computer science (2019) and has been actively involved in research, teaching, and supervision since 2020. Education : Bachelor's (2014), Master's (2016), and PhD (2019) in applied mathematics and computer science from ITMO University. Professional Activity : 2020–present – Assistant, Faculty of Information Technologies and Programming (ITMO University); 2016 – Programmer, ITMO University; 2014–2017 – Software Engineer, LLC Special Technology Center. Research Interests : Focus on machine learning optimization, clustering techniques, and time series forecasting. His work spans algorithm development and practical applications in data analysis. Scientific Award : St. Petersburg Government scholarship (2017). Responsibilities : Conducting research, publishing papers, teaching, and supervising students.

• Big Data Processing
• Computational Science
• Distributed Computing
• +3 more

Denis Nasonov is an Associate Professor at ITMO University's Faculty of Digital Transformation and Senior Research Associate at the National Center for Cognitive Research. He leads the Research Laboratory "Cognitive Technologies in Industry" and contributes to TROIKA: Technology and Research On Information-Driven Knowledge Alliance since 2013. Graduated from ITMO University with a major in Applied Mathematics and Computer Science (2010) Specializes in Big Data processing, workflow scheduling algorithms, and urgent computing infrastructure Active in computational science research with focus on distributed environments and metaheuristic optimization His research spans computational science, distributed computing, and environmental modeling, focusing on hybrid scheduling algorithms for early warning systems and urgent computing applications. Email: dnasonov@itmo.ru

• Computing and applied mathematics
• Integral equations and applications
• Ill-posed problems and regular methods for their solving
• +5 more

Valeriy Sizikov is a Professor at the Faculty of Software Engineering and Computer Systems of ITMO University, with a Doctor of Sciences degree in Technics (1989). He has held academic positions at ITMO University since 1992 and at Saint Petersburg University (SPU) since 2008. Research focuses on computing and applied mathematics Specializes in integral equations, tomography, and ill-posed problems Develops stable methods for measurement data processing His work spans mathematical modeling, signal processing, and programming applications in physics and engineering. A prolific author, Sizikov has published over 120 scientific works, including textbooks on inverse problems and MATLAB implementations. Awarded grants from prestigious institutions including: Soros Foundation (2000) Russian Foundation for Basic Research (2000, 2005, 2009, 2013) IMACS (2000) ISSEP (2004) He has presented at major international conferences like ICM, ENUMATH, and OSAV, and teaches courses in inverse problems, tomography, and measurement theory.

• Machine Learning
• Deep Learning
• Generative Adversarial Networks
• +3 more

Ali Saraa is a Visiting Lecturer and Postgraduate Student at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science, Institute of Artificial Intelligence and Digital Sciences, and the Research and Educational Laboratory of Big Data Analysis Methods. She began her work at HSE in 2021 and is currently in her 3rd year of postgraduate studies. Her educational background includes a Master's degree in Mechatronics and Robotics from Moscow State Technological University "Stankin" (2021) and a Bachelor's degree in Electromechanical Engineering from Tishreen University (2018). Dr. Saraa's research focuses on machine learning , deep learning , and generative adversarial networks , with applications spanning physics instrumentation, electrical engineering, safety monitoring, and educational technology. Her work on personalized adaptive learning systems involves developing methods and algorithms for building personal learning trajectories, from Bayesian Knowledge Tracing (BKT) up to modern machine learning approaches. She conducts detailed analysis of current implementations of methods and algorithms in various Personalized Adaptive Learning Systems (PALSs), examining statements, input data, results, ideas, solutions, software implementation details, efficiency, and adoption. Her recent publications demonstrate a strong trend toward applying deep learning techniques to solve practical problems across multiple scientific and engineering domains. She has made significant contributions in fault detection for electrical systems, calorimeter response correction in particle physics, safety monitoring through PPE detection, and video stream processing for robotic systems. Letter of gratitude from the Senior Director for Research and Development at HSE (August 2024) As a Postgraduate Student, Ali Saraa is working on her dissertation titled "Efficient parameterizations for training and retraining generative adversarial networks in the image and audio domain" under the supervision of Denis Aleksandrovich Derkach. She has co-authored multiple publications, contributed to research projects at the Institute for Information Transmission Problems (IITP RAS), and holds two patents related to AI applications in industrial settings. Dr. Saraa is affiliated with the Research and Educational Laboratory of Big Data Analysis Methods within the Institute of Artificial Intelligence and Digital Sciences at HSE. Her work bridges theoretical machine learning research with practical applications in physics, electrical engineering, robotics, and educational technology, demonstrating the interdisciplinary nature of modern AI research.

• stochastic approximation
• reinforcement learning
• sampling
• +4 more

Sergey Vladimirovich Samsonov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as Head of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within the Institute of Artificial Intelligence and Digital Sciences. He is affiliated with the Department of Big Data and Information Retrieval and the Joint Department of the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences. Samsonov began his tenure at HSE in 2018 and has accumulated 7 years of scientific and teaching experience. His educational background includes a PhD from HSE (2024) and a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017). Samsonov's research focuses on stochastic approximation, reinforcement learning, sampling techniques, Markov chain Monte Carlo (MCMC) methods, and multivariate statistics . His work bridges theoretical mathematics with practical machine learning applications, particularly in developing algorithms with strong theoretical guarantees. He has made significant contributions to understanding convergence properties of stochastic algorithms and developing variance reduction techniques. Analysis of his recent publications reveals a consistent focus on the mathematical foundations of machine learning, particularly in stochastic approximation methods, reinforcement learning theory, and generative modeling. His work often combines rigorous theoretical analysis with practical applications, demonstrating expertise in both pure mathematics and applied machine learning. The publication venues (including top conferences like NeurIPS, ICLR, and AISTATS) reflect the high impact and quality of his research in the machine learning community. Young Scientist Badge (December 2024) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of gratitude from the Faculty of Computer Science at HSE (September 2021) Rector's personal allowance (2022-2023) Numerous bonuses for high-impact publications (2021-2027) Best Teacher award (2024-2025, 2022, 2020) Segalovich Scientific Prize (2022) National Prize 'Leaders in AI - 2024' Samsonov teaches advanced courses including Markov Chains, Sampling and Generative Modeling, and Matrix Computations. His laboratory work focuses on developing stochastic algorithms for machine learning applications. He has been involved in HSE's collaboration with Sber, which has resulted in 19 successfully implemented AI projects since 2021. His research has gained significant recognition, with multiple papers accepted at top-tier conferences including 12 papers presented at NeurIPS in recent years.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +5 more

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

• topological data analysis
• machine learning
• mathematical statistics
• +6 more

Maxim Evgenievich Beketov is a Research Fellow at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2020. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, contributing to cutting-edge research in computational methods and artificial intelligence. His educational background includes: Master's degree (2017) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor's degree (2015) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Beketov's research spans multiple interdisciplinary fields with a strong mathematical foundation. His primary interests include topological data analysis, machine learning, mathematical and Bayesian statistics, differential geometry, and computational neuroscience. He applies these methods to problems in dimensionality reduction, variety assessment, and graph neural networks. His work bridges theoretical mathematics with practical applications in artificial intelligence and neuroscience, particularly in understanding cognitive processes through topological approaches. An analysis of his recent publications reveals a strong focus on topological methods in machine learning, with increasing emphasis on applications to neuroscience and cognitive mapping. His work demonstrates a progression from theoretical mathematical foundations toward practical implementations in spiking neural networks, traffic control systems, and music information retrieval. The interdisciplinary nature of his research connects computer science, mathematics, and neuroscience through topological approaches. His scientific achievements include: High Professional Potential Group (HSE Personnel Reserve), Category: New Researchers (2025) Beketov has been actively involved in academic teaching, offering courses including Introduction to Discrete Differential Geometry and Mathematical Analysis. His research is supported through the HSE University Basic Research Program, as acknowledged in his publications. He collaborates with researchers across multiple institutions, as evidenced by his co-authorship on papers with numerous collaborators. He is a core member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, where he contributes to projects involving topological data analysis, machine learning, and computational neuroscience. His work in the laboratory focuses on developing advanced mathematical methods for analyzing complex data structures, with applications ranging from cognitive neuroscience to transportation systems.

• Reinforcement Learning
• Probability Theory
• Stochastic Algorithms
• +3 more

Ilya Valerievich Levin serves as a Junior Research Fellow at the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis and as a Teacher in the Department of Big Data and Information Retrieval, both within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE). He began his tenure at HSE in 2020 and has accumulated 3 years of scientific and teaching experience. His primary affiliations include the Institute of Artificial Intelligence and Digital Sciences where he works under the supervision of A. A. Naumov. ORCID: 0000-0002-9708-8189 Researcher ID: GZL-7944-2022 Location: Pokrovsky Boulevard, 11, office T926 Office Hours: 12:00-18:00 Levin's research expertise centers on reinforcement learning methodologies, stochastic approximation algorithms, and multidimensional data analysis. His work bridges theoretical foundations with practical applications in machine learning systems, particularly focusing on federated learning environments and evaluation frameworks for reinforcement learning algorithms. His research demonstrates strong mathematical foundations with applications across distributed computing systems. His publication record shows a clear trajectory in advancing reinforcement learning techniques, with recent work presented at NeurIPS 2024 addressing heterogeneity challenges in federated learning systems. His research combines theoretical rigor with practical implementation concerns, reflecting the interdisciplinary nature of modern artificial intelligence research. Letter of gratitude from the Faculty of Computer Science (July 2024) Bonus for publication in List A journal (2025-2026) HSE Personnel Reserve - New Researchers category (2022) Levin has been actively involved in teaching Probability Theory, Mathematical Statistics, and Mathematical Foundations of Reinforcement Learning across multiple academic programs including bachelor's and master's degrees. His teaching spans both Russian and English language instruction formats, including specialized programs like Mago-Lego. He has consistently taught core mathematical courses for computer science students since the 2022/2023 academic year, demonstrating commitment to foundational education in the field.

Alexander Alexandrovich MolozhavenkoView profile

Researcher

• computational mathematics
• artificial intelligence
• matrix computations
• +2 more

Alexander Alexandrovich Molozhavenko serves as a Research Intern at the Research and Educational Laboratory of Matrix and Tensor Methods in Machine Learning within the Institute of Artificial Intelligence and Digital Sciences at the National Research University Higher School of Economics (HSE). He also holds a teaching position in the Department of Big Data and Information Retrieval at HSE's Faculty of Computer Science. He began his employment at HSE in 2025. His educational background includes a Bachelor's degree from Moscow Institute of Physics and Technology (2023) in Applied Mathematics and Physics. His primary research interests focus on computational mathematics and artificial intelligence, with specific expertise in matrix and tensor computations. Molozhavenko teaches several courses including Fundamentals of Matrix Computations for both Bachelor's and Master's programs, as well as Fundamentals of Tensor Computing. His published research includes work on optimization techniques for tensor train manifolds, demonstrating his specialization in advanced mathematical approaches to machine learning problems. His professional experience prior to HSE includes work at the Laboratory of Advanced Combinatorics and Network Applications at MIPT, and roles as Junior Lecturer in Reinforcement Learning and Optimization.

• Python programming
• data analysis
• deep learning
• +3 more

Bedak Ivan Andreevich is a Lecturer at the National Research University Higher School of Economics (HSE University), holding dual positions in the Graduate School of Business/Department of Business Informatics and as Project Manager in the Faculty of Computer Science/Institute of Artificial Intelligence and Digital Sciences/Department of Applied Technological Solutions. He joined HSE University in 2024. Education: 2024: Bachelor's degree in Applied Informatics from Financial University under the Government of the Russian Federation, qualification "Bachelor" Research and Professional Interests: Bedak Ivan Andreevich specializes in Python programming, data analysis, deep learning, and machine learning. His expertise is reflected in the courses he teaches including Algorithms and Data Structures, Python Programming, and Data Processing Technologies. His work bridges business informatics with artificial intelligence applications, focusing on practical implementation of data science techniques in business contexts. Current Positions: Lecturer, Graduate School of Business, Department of Business Informatics Project Manager, Faculty of Computer Science, Institute of Artificial Intelligence and Digital Sciences, Department of Applied Technological Solutions Previous Experience: July 2023 - Present: CIARS LLC, Artificial Intelligence and Network Analysis Laboratory August 2021 - September 2022: Yandex LLC, Search Quality Assessment Group (Business Support) Professional Activities: Bedak Ivan Andreevich teaches multiple courses at HSE University including Algorithms and Data Structures, Python Programming, and Data Processing Technologies for Business Informatics students. He works under the supervision of Zaramenskikh E. P. and maintains professional profiles on ORCID (0009-0001-3134-4661), ResearcherID (A-1009-2008), and Google Scholar.

• Deep Learning
• Computer Vision
• Generative Models
• +3 more

Foma Aleksandrovich Shipilov is a Lecturer and Research Intern at the National Research University Higher School of Economics (HSE), working within the Faculty of Computer Science, specifically in the Department of Big Data and Information Retrieval and the Institute of Artificial Intelligence and Digital Sciences. He began his tenure at HSE in 2025 and is actively involved in both teaching and research activities. Shipilov holds a Master's degree in "Applied Mathematics and Computer Science" from HSE (2025) and attended the Summer School on Machine Learning in Bioinformatics in 2022. His educational background has prepared him for his current roles in academia and research. His primary research interests include Deep Learning, Computer Vision, and Generative Models, with applications extending to Physics and Particle Detection systems. Shipilov's work demonstrates the interdisciplinary nature of modern machine learning applications, bridging computer science with physics research. His publications focus on applying machine learning techniques to improve particle detector systems, particularly focusing aerogel detectors used in high-energy physics experiments. Letter of gratitude from the Senior Director for Research and Development at HSE (August 2024) Ilya Segalovich Scholarship (2022) Grant of the President of the Russian Federation (2019) Shipilov teaches courses including "Introduction to Deep Learning" and "Deep Learning 1" for undergraduate programs at HSE across multiple disciplines including Applied Mathematics and Computer Science, Psychology, Public Administration, Sociology, Political Science, and Economics. Previously, he served as a Teaching Assistant for Discrete Mathematics, Algorithms and Data Structures, and Deep Learning courses. His industry experience includes a position as Assistant Engineer at Huawei Noah's Ark Lab (2020-2021), where he worked on deep learning applications for video remastering and photographic super-resolution. Shipilov is affiliated with the Research and Educational Laboratory of Big Data Analysis Methods and participates in the annual machine learning seminars held at the Voronovo Educational Center, which facilitate networking and knowledge exchange among students, faculty, and research staff.

Marina Evgenievna SheshukovaView profile

Research Fellow

• Statistics
• Markov Chains
• Mathematical Statistics
• +3 more

Marina Evgenievna Sheshukova serves as a Junior Research Fellow at the Faculty of Computer Science, National Research University Higher School of Economics (HSE), specifically within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. She also holds a teaching position in the Department of Big Data and Information Retrieval. Dr. Sheshukova joined HSE in 2021 and has been actively contributing to both research and educational activities. Faculty of Computer Science Institute of Artificial Intelligence and Digital Sciences International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis Department of Big Data and Information Retrieval Dr. Sheshukova completed her Bachelor's degree in 2023 at the National Research University Higher School of Economics, majoring in Applied Mathematics and Computer Science. Her academic journey reflects a strong foundation in mathematical and computational disciplines, which has shaped her current research trajectory. Dr. Sheshukova's primary research interests focus on statistics, particularly Markov chains and related stochastic processes. Her work bridges theoretical mathematics with practical applications in machine learning and data analysis. She has made significant contributions to the understanding of stochastic gradient descent methods, optimization algorithms with Markovian noise, and statistical properties of Markov chain models. Her research demonstrates a strong mathematical foundation with applications to modern computational challenges in artificial intelligence. Dr. Sheshukova's publication record shows a clear focus on theoretical aspects of machine learning and statistical methods. Her recent work examines nonasymptotic properties of optimization algorithms, particularly those involving stochastic gradient descent with extrapolation techniques. She has also made contributions to the theory of first-order optimization methods with Markovian noise and established important inequalities for statistics of Markov chains. These publications reflect her expertise at the intersection of probability theory, optimization, and machine learning theory. Letter of gratitude from the Faculty of Computer Science at HSE (July 2025) Bonus for publication in a journal from List A (2025–2026) Dr. Sheshukova has received recognition for her scholarly contributions, including a letter of gratitude from the Faculty of Computer Science and a publication bonus for her work in high-impact venues. While specific grant information isn't detailed in the available materials, her participation in conference publications at premier venues like ICLR and NeurIPS suggests successful competitive funding. She serves as a teacher for multiple courses, indicating her involvement in academic mentoring and instruction. Dr. Sheshukova is affiliated with the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, where she conducts research under the supervision of Naumov A. A. This laboratory appears to focus on theoretical aspects of data science, stochastic processes, and algorithm development, providing a collaborative environment for her research in Markov chains and statistical learning theory.