جستجوی استاد راهنمای دانشگاهی

Shvedov Aleksey Sergeevich is a Professor at the Faculty of Economic Sciences of the National Research University Higher School of Economics (HSE), affiliated with the Department of Applied Economics. He has been working at HSE since 1993 with 44 years of scientific and teaching experience. His office is located at Pokrovsky Boulevard, 11, office S524 in Moscow. Professor (1997) Doctor of Physical and Mathematical Sciences (1992) Specialist in Mathematics from Lomonosov Moscow State University, Faculty of Mechanics and Mathematics (1978) Additional education at London School of Economics (1998) and Sorbonne (2000) Professor Shvedov's research focuses on the intersection of fuzzy logic, probability theory, and economic applications. His work primarily explores fuzzy-random optimization, econometric analysis, mathematical economics, and random matrix theory. He has developed innovative approaches to modeling economic phenomena using fuzzy-probability analysis, particularly in financial time series, game theory applications, and regression models with fuzzy data. His research bridges theoretical mathematics with practical economic applications, especially in financial markets and decision-making under uncertainty. His recent publications (2020-2024) demonstrate a consistent focus on advancing fuzzy set theory applications in economics, with particular emphasis on game theory (Cournot and Bertrand oligopoly models with fuzzy parameters), statistical decision theory with fuzzy losses, and financial modeling using fuzzy systems. The work shows increasing sophistication in handling fuzzy-random variables and developing computational methods for economic applications. Scientific awards and recognition: Medal "Recognition - 25 years of successful work" (2023) Honorary Worker of Higher Professional Education of the Russian Federation (2016) Certificate of Honor from Ministry of Education and Science (2012) Multiple academic work allowances and publication bonuses Prize from Moscow Mathematical Society (1987) Inclusion in Who's Who in the World (2016) Professor Shvedov has supervised multiple doctoral dissertations, including works on portfolio theory and covariance matrix estimation. He has led research grants, notably an RFBR project on computational financial mathematics (2000-2002). His teaching portfolio includes advanced courses in probability theory, statistical analysis of financial time series, and specialized courses on fuzzy-probability analysis and financial market models. He has been actively presenting research at major international conferences, particularly the annual conferences named after S.A. Ayvazyan.

• Fuzzy-random optimization
• Econometric analysis
• Mathematical Economics
• +۳ مورد دیگر

Denis Romanovich Rakitin serves as a Research Fellow and Senior Lecturer at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University), where he has been employed since 2020. He is affiliated with both the Institute of Artificial Intelligence and Digital Sciences and the Center for Deep Learning and Bayesian Methods, contributing to HSE's growing prominence in AI research. Rakitin completed his Master's degree in Applied Mathematics and Computer Science at HSE in 2022 and is currently in his third year of postgraduate studies. His dissertation focuses on computational methods for solving the Schrödinger bridge problem under the supervision of Professor Dmitry Petrovich Vetrov. His academic journey reflects a strong foundation in mathematical and computational disciplines applied to cutting-edge AI research problems. His research interests center around Bayesian methods, machine learning, and probability theory, with significant contributions to generative models, differentiable rendering, and probabilistic approaches to AI challenges. His teaching portfolio includes advanced courses such as "Generative Models Based on Diffusion" and "Probability Theory," for which he was recognized as "Best Teacher of 2023." Rakitin's publication record demonstrates expertise in cutting-edge areas of machine learning, with research presented at top-tier conferences including NeurIPS, ICML, and AISTATS. His work spans computer vision, graphics, and combinatorial optimization, reflecting the interdisciplinary nature of modern AI research. Letter of gratitude from the Faculty of Computer Science at HSE (July 2023) Academic Work Allowance (2024-2025) Bonus for publication in a journal from List A (2025-2026, 2023-2024) Best Teacher of 2023 High Professional Potential Group (HSE Personnel Reserve) Category 'New Researchers' (2022-2023) Yandex Scholarship Award (2025) As a member of the Center for Deep Learning and Bayesian Methods, Rakitin actively participates in academic events such as the workshop "The Mathematics of Machine Learning," where researchers explore mathematical aspects underlying modern machine learning approaches. His recent recognition with the Yandex Scholarship highlights his significant contributions to research in computer science and positions him as a rising figure in Russia's AI research community.

• Bayesian methods
• Machine learning
• Probability theory
• +۲ مورد دیگر

Maksimov Yan Viktorovich serves as a Research Intern at the Center for Deep Learning and Bayesian Methods and as a Visiting Lecturer in the Department of Big Data and Information Retrieval within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE). He began his tenure at HSE in 2025 and operates under the supervision of Alanov A. His office is located at Pokrovsky Boulevard Building 11, Office S822, with availability from 11:00 AM to 7:00 PM. His research focuses on neural networks and deep learning methodologies, with particular emphasis on optimization techniques and architectural innovations in machine learning systems. Key interests include flow network theory, feature mapping across neural layers, and probabilistic modeling approaches. His work bridges theoretical computer science with practical applications in artificial intelligence. Analysis of his publications reveals a strong trend in advancing foundational machine learning theory, particularly in improving decision-making processes in discrete environments (2025) and enhancing model interpretability through cross-layer feature analysis (2024). His research sits at the intersection of deep learning theory and practical implementation challenges. Yandex Scholarship (2025) Maksimov teaches 'Optimization Methods in Machine Learning' for both the Faculty of Computer Science and Faculty of Economic Sciences, covering Applied Mathematics and Computer Science programs. His professional development includes prior research internships at Yandex Technologies (2023) as a Machine Learning Developer Intern and Tinkoff AI Lab (2024) as an NLP Research Intern. He actively participates in HSE's Institute of Artificial Intelligence and Digital Sciences workshops, including the 2025 'Mathematics of Machine Learning' event focusing on mathematical foundations of AI.

• Neural Networks
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Alexander Mikhailovich Shabalin is a Visiting Lecturer and Research Intern at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science, Institute of Artificial Intelligence and Digital Sciences, Center for Deep Learning and Bayesian Methods, and Department of Big Data and Information Retrieval. He began working at HSE in 2020 and is currently in his second year of postgraduate studies. Shabalin holds a Master's degree (2023) and Bachelor's degree (2021) in Applied Mathematics and Computer Science from HSE. His approved dissertation topic is "Application of diffusion models to text data" under the supervision of Dmitry Petrovich Vetrov. His primary research interests focus on Natural Language Processing and self-supervised learning, with particular emphasis on diffusion models for text data. His work bridges theoretical machine learning concepts with practical applications in text analysis and generation. The research trajectory evident in his publications shows a progression from foundational machine learning concepts toward more specialized applications of diffusion models in language processing. Among his notable achievements are contributions to three significant Massive Open Online Courses (MOOCs) including "Modern Data Analysis: Machine Learning," "Fundamentals of Deep Learning," and "Advanced Deep Learning Methods," demonstrating his commitment to education and knowledge dissemination in artificial intelligence. As an educator, Shabalin teaches "Deep Learning for Text Data" across multiple programs including Applied Mathematics and Computer Science and Economics. His teaching experience spans various faculties and demonstrates versatility in delivering technical content to diverse student populations. His professional background includes research at the Center for Deep Learning and Bayesian Methods since 2021, along with previous development experience at Yandex.Cloud and Yandex ML-landing where he worked on infrastructure development and reinforcement learning applications for music recommendations.

• Natural Language Processing
• Self-supervised Learning
• Diffusion Models
• +۳ مورد دیگر

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

• Automatic text analysis
• Information search
• IT project management
• +۲ مورد دیگر

Pavel Konstantinovich Katyshev is a Professor at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Economic Sciences and Department of Applied Economics. With 52 years of scientific and teaching experience, he has been working at HSE since 2009 (and previously from 1997-1999). His academic career spans multiple prestigious institutions including the Central Economic-Mathematical Institute of the Russian Academy of Sciences (1975-present), Lomonosov Moscow State University, and the Russian Economic School. Professor Katyshev's educational background includes: 1976: Candidate of Physical and Mathematical Sciences (specialty 01.01.05 "Probability Theory and Mathematical Statistics") 1975: Postgraduate studies at Moscow State University M.V. Lomonosov 1971: Specialty "Mathematics" with qualification "Mathematician" from Lomonosov Moscow State University His primary research interests focus on the intersection of mathematics and economics, particularly in econometrics, probability theory, and mathematical economics. Professor Katyshev specializes in time series analysis, random error term models, cointegration, controlled random processes, and stochastic models of economic dynamics and equilibrium. His work bridges theoretical mathematical frameworks with practical economic applications, making significant contributions to both fields. His recent publications demonstrate a consistent focus on econometric methods and their applications to the Russian economy, particularly in analyzing relationships between energy markets (oil, electricity) and economic growth. He has developed numerous educational resources including textbooks and problem collections in probability theory, mathematical statistics, and econometrics, which have become standard references in Russian economic education. Professor Katyshev has received numerous accolades for his contributions: Medal "Recognition - 15 years of successful work" of the National Research University Higher School of Economics (October 2024) Letter of thanks from the Rector of HSE (October 2023) Gratitude from the Vice-Rector of HSE (October 2021) Honorary Diploma of the Higher School of Economics (December 2018) Gratitude from the Higher School of Economics (December 2012) Best Teacher award for 2023-2025 and 2011-2014 As an educator, Professor Katyshev has supervised students including Bogomolov Andrey Vasilievich (2015), whose thesis focused on parameter estimation in models with random error duration. He teaches courses in Probability Theory and Mathematical Statistics across various programs at HSE, including bachelor's and master's degrees in Economics. His teaching approach has been praised by students as "so cool and useful" that "you just listen to these lectures." Professor Katyshev previously served as head of the International Laboratory of Quantitative Finance at HSE (2014-2015) and has been involved with the Moscow Institute of Economics and Finance (MIÉF) as a statistics lecturer from 1997-2014. His extensive publication record includes more than 30 printed works, translations of three books from English, and co-authorship of the widely used textbook "Econometrics. Basic course."

• Econometrics
• Time Series Analysis
• Probability Theory
• +۵ مورد دیگر

Alexander Vladimirovich Gasnikov is a Leading Researcher at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science, the Institute of Artificial Intelligence and Digital Sciences, and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. He has been working at HSE since 2015 and has accumulated 17 years of scientific and teaching experience. Gasnikov received his academic credentials from the Moscow Institute of Physics and Technology (State University). He earned his Bachelor's degree in Applied Mathematics and Physics in 2004, followed by a Master's degree in the same specialty in 2006. He completed postgraduate studies at MIPT from 2006-2007, earned his Candidate of Physical and Mathematical Sciences degree in 2007, was awarded the academic title of Associate Professor in 2011, and ultimately received his Doctor of Physical and Mathematical Sciences degree in 2016 with a dissertation on 'Efficient Numerical Methods for Finding Equilibria in Large Transport Networks.' Gasnikov's research focuses on convex optimization, mathematical modeling of traffic flows, and Markov processes. His work bridges theoretical mathematics with practical applications in optimization algorithms and transportation science. He has made significant contributions to developing efficient numerical methods for optimization problems, particularly in the context of large-scale networks and stochastic settings. His research has evolved from traditional optimization methods to more advanced techniques involving stochasticity, decentralization, and applications to machine learning problems. His publications demonstrate expertise in both theoretical analysis (establishing convergence rates, lower bounds) and practical algorithm development across multiple prestigious venues including Journal of Optimization Theory and Applications, SIAM Journal on Optimization, and top machine learning conferences like NeurIPS. Gasnikov has received recognition including a bonus for publication in international peer-reviewed journals (2019-2021) and has served as a member of the editorial board of the 'Siberian Journal of Computational Mathematics' since 2017. As a supervisor, Gasnikov has guided multiple doctoral candidates, including Alexander Ogaltsov, Titov A. A. (2023), and Tyurin A. I. (2020) for Candidate of Sciences degrees, and Dvurechensky P. E. (2020) for a Doctor of Science degree. His research has been supported by grants from the President of Russia (MD-1320.2018.1) and the Russian Foundation for Basic Research (18-31-20005 mol_a_ved). Gasnikov's work contributes significantly to the theoretical foundations of optimization algorithms that power modern artificial intelligence systems. His leadership in the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis positions him at the forefront of research connecting mathematical optimization with practical applications in machine learning and data science.

• convex optimization
• mathematical modeling of traffic flows
• Markov processes
• +۳ مورد دیگر

Daniil Nikolaevich Tyapkin is a Junior Research Fellow and Postgraduate Student at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science. He works within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis under the supervision of Alexey Alexandrovich Naumov. Education: 2021: Bachelor's degree in Applied Mathematics and Computer Science from HSE Currently in 2nd year of postgraduate studies with dissertation topic: "Randomized trial for reinforcement learning" Research Interests: His work focuses on reinforcement learning and stochastic optimization, including exploration strategies, generative flow networks, and theoretical analysis of learning algorithms. He develops efficient methods for complex decision-making problems with applications in AI and data science. Publications: His research appears in top venues (ICLR, NeurIPS, ICML, AISTATS) covering reinforcement learning, optimization, and probabilistic modeling. Recent works explore GFlowNets, entropy regularization, and high-probability bounds for learning algorithms. Awards & Honors: Letter of gratitude from HSE Faculty of Computer Science (2022) Publication bonus for List A journal articles (2023-2024) HSE Personnel Reserve - New Researchers category (2022-2023) Teaching & Advising: Teaches courses in Mathematical Statistics and Matrix Computations. No formal student advising mentioned. Laboratory Affiliation: Member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, focusing on algorithmic development for machine learning challenges.

• Reinforcement Learning
• Stochastic Optimization

Aliyev Mishan Hammad oglu is a Lecturer at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science, Department of Big Data and Information Retrieval, and the Institute of Artificial Intelligence and Digital Sciences. He works in the Laboratory of Theoretical Foundations of Artificial Intelligence Models and the Yandex Research and Education Laboratory. Aliyev began his position at HSE in 2025 and serves as both an Expert and Teacher within the institution. His educational background includes a Master's degree in Applied Mathematics and Computer Science from HSE (2024) and a Bachelor's degree in the same field from MIREA - Russian Technological University (2022). Currently in his first year of postgraduate studies, he is working on his dissertation titled "Application of generative models for three-dimensional reconstruction and generation" under the supervision of Kirill Alekseevich Struminsky. Aliyev's research focuses on generative models, particularly for generating textures for 3D models, machine learning, Bayesian methods, and deep learning. His teaching portfolio includes courses such as Deep Learning 2, Text Analysis with Generative Models, and Deep Learning 1 for both Computer Science and Economics students. His recent publications demonstrate expertise in both computer graphics applications of AI and computational epidemiology. Among his professional achievements, Aliyev was recognized as "Best Teacher" in 2025. He serves as a Research Intern at the Center for Deep Learning and Bayesian Methods since 2024 and has participated in notable events including AI meetups between HSE's Faculty of Computer Science and Yandex in Minsk, and academic presentations at venues like the "Rovesnik" bar lecture series. Best Teacher - 2025 Aliyev actively contributes to academic life through teaching, research, and participation in laboratory meetings such as the "Laboratory" series where researchers connect with students. His work bridges theoretical AI foundations with practical applications in 3D modeling and texture generation.

• Generative Models
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Egor Felixovich Chimbulatov serves as a Research Intern at the Center for Deep Learning and Bayesian Methods and as a Teacher in the Department of Big Data and Information Retrieval at the National Research University Higher School of Economics (HSE), Faculty of Computer Science. He began his work at HSE in 2024 and is affiliated with the Institute of Artificial Intelligence and Digital Sciences. Chimbulatov completed his Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University in 2023. His professional interests focus on diffusion models and their application in text generation, representing cutting-edge research in artificial intelligence. His research output includes work on understanding diffusion model properties in language model encodings, demonstrating expertise at the intersection of generative AI and natural language processing. While early in his career, his publication record shows promising contributions to the field of machine learning. Chimbulatov has been recognized for his potential, being selected for the High Professional Potential Group in the HSE Personnel Reserve under the 'New Researchers' category for 2025. High Professional Potential Group (HSE Personnel Reserve) Category: New Researchers (2025) As an educator, Chimbulatov teaches advanced courses including Bayesian Methods in Machine Learning and Deep Learning for Text Data to fourth-year undergraduate students in both Applied Mathematics and Computer Science and Economics programs. His teaching responsibilities indicate strong technical expertise in machine learning methodologies and their practical applications. He works under the supervision of Alanov A. at the Center for Deep Learning and Bayesian Methods, which hosted its fifth 'Laboratory' meeting in December 2024 where students had the opportunity to explore the center's research activities. Chimbulatov maintains professional profiles through ORCID (0009-0003-8638-7102), ResearcherID (LQJ-8752-2024), and Google Scholar, demonstrating his commitment to academic transparency and scholarly communication.

• Diffusion Models
• Text Generation
• Machine Learning
• +۳ مورد دیگر

Olga Anatolyevna Demidova is a Professor at the National Research University Higher School of Economics (HSE), working within the Faculty of Economic Sciences and the Department of Applied Economics. She also serves as Head of the Scientific and Educational Laboratory of Spatial-Econometric Modeling of Socio-Economic Processes in Russia. With over 35 years of scientific and teaching experience, including 27 years at HSE since 1998, Professor Demidova has established herself as a leading expert in spatial econometrics and regional economic analysis in Russia. 2022: Doctor of Economics from National Research University Higher School of Economics 2012: Academic title of Associate Professor 1999: Master's degree in Economics from State University - Higher School of Economics 1995: Candidate of Physical and Mathematical Sciences from A.A. Dorodnitsyn Computing Center of the Russian Academy of Sciences 1989: Specialist in Mathematics from Lomonosov Moscow State University, Faculty of Mechanics and Mathematics Professor Demidova's primary research interests include spatial econometrics, Bayesian approaches, microeconometrics, nonparametric estimation methods, and comparative economic research. Her work focuses on analyzing regional economic processes in Russia, with particular attention to spatial dependencies, regional disparities, and the effects of economic policies across different Russian regions. She has developed specialized methodologies for spatial-econometric modeling that have been widely applied in Russian economic research. Professor Demidova's recent publications (2021-2025) demonstrate a consistent focus on spatial-economic analysis of Russian regions. Her research examines monetary policy transmission mechanisms, regional government spending effects, income inequality impacts, and electoral patterns through the lens of spatial econometrics. A notable trend is her increasing focus on crisis effects, particularly analyzing the impact of the COVID-19 pandemic on household consumption across Russian regions. Her work consistently applies advanced spatial modeling techniques to understand regional economic dynamics in Russia's heterogeneous economic landscape. 2022: Honorary title "Honored Worker of the Sphere of Education of the Russian Federation" 2018: "Recognition - 20 Years of Successful Work" Medal from HSE 2016: Honorary Certificate from Higher School of Economics 2013 & 2012: Gratitude from Higher School of Economics 2022-2025: Bonus for defending doctoral dissertation 2023-2025: Bonuses for publications in journals from List B Professor Demidova has supervised numerous graduate students, including nine doctoral candidates (as of 2025), with several having successfully defended their dissertations. Her current PhD students are working on topics related to electoral preferences, business performance, unemployment, mortgage rates, and regional inequality. She has led multiple research grants, including a 2020-2021 RFBR grant project "Modeling the mutual influence of Russian regions and assessing the effectiveness of government programs using spatial-econometric methods." Her research has been supported by various HSE individual research projects since 2010, focusing on spatial effects in regional economic analysis. As Head of the Scientific and Educational Laboratory of Spatial-Econometric Modeling of Socio-Economic Processes in Russia, Professor Demidova leads a research team that develops and applies spatial-econometric methodologies to analyze regional economic processes in Russia. The laboratory conducts research on regional convergence, electoral behavior, monetary policy transmission, and the effects of government spending across Russian regions. In 2024, the laboratory initiated a "Mirror Laboratory" project with the Ufa University of Science and Technology to enhance spatial-econometric models considering hierarchical data structures and heterogeneous process dynamics.

• Spatial Econometrics
• Bayesian Methods
• Microeconometrics
• +۵ مورد دیگر

Natalia Sergeevna Belova is an active Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University). She has been with HSE since 2012, accumulating 13 years of scientific and teaching experience. Her academic journey began with engineering education and progressed through postgraduate studies to earning her Candidate of Technical Sciences degree. Her educational background includes: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specializing in Mathematical and Software Support for Computing Machines 2009: Postgraduate studies at the same institution 2005: Engineering degree from Moscow State Academy of Instrument Engineering and Computer Science Belova's research interests span automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work demonstrates a strong focus on practical applications of computer science, particularly in face recognition, pattern recognition, and educational methodologies for software engineering. She has made significant contributions to the fields of embedded database systems and computer vision. Her publication record shows a clear evolution from foundational work on embedded databases (2009) to advanced research in computer vision and deep learning (2015-2025). The most recent publications focus on artificial intelligence applications in transport design and affect recognition in video, demonstrating her ability to adapt to emerging technologies while maintaining expertise in her core areas. Among her notable achievements: Gratitude from HSE University leadership (2023, 2025) Multiple publication bonuses for high-impact research Recognition as Best Teacher (2016-2017) Membership in the High Professional Potential Group (HSE personnel reserve) Belova has supervised numerous bachelor's theses, guiding students through projects ranging from mobile applications to complex software systems. She has also secured significant research funding, including a Presidential Grant for young doctors of science (2017-2018) for developing pattern recognition methods. Her teaching portfolio includes courses on Group Dynamics and Communication in Software Engineering Professional Practice and Software Engineering Economics, reflecting her dual expertise in technical and soft skills development for future software engineers.

• automatic text analysis
• information search
• IT project management
• +۶ مورد دیگر

Ignatov Andrey Dmitrievich is a lecturer at the Faculty of Computer Science , Department of Software Engineering at National Research University Higher School of Economics (HSE), since 2019. He holds a postgraduate degree in Informatics and Computer Engineering (2023) and Master's in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2019). Lecturer in Software Engineering and Applied Mathematics programs Conducts research in combinatorial optimization and protein structure modeling Recipient of multiple teaching awards including Best Teacher (2023, 2025) His research focuses on global optimization , parallel computing , and computational biology with applications to cloud scheduling and protein modeling. Publications demonstrate expertise in heuristic algorithms , branch-and-bound optimization , and BOINC-based distributed computing . Recent teaching includes courses on Algorithms and Data Structures, Protein Structure Modeling, and Optimization Methods. 2023-2024, 2024-2025: Allowance for young teachers 2023: Gratitude from Faculty of Computer Science 2024: Category 'New Lecturers' 2025: Co-inventor of legal assistance software patent

• Global Optimization
• Augmented Reality
• Computational Biology
• +۳ مورد دیگر

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.

• Machine Learning
• Deep Learning
• Generative Adversarial Networks
• +۳ مورد دیگر

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.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +۵ مورد دیگر