جستجوی استاد راهنمای دانشگاهی

• Automatic text analysis
• Information search
• IT project management
• +۲ 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).

• Econometrics
• Financial Modeling
• Sample Selection Models
• +۳ مورد دیگر

Bogdan Stanislavovich Potanin serves as an Associate Professor at the Department of Applied Economics , Faculty of Economic Sciences , National Research University Higher School of Economics . He also holds a Senior Research Fellow position at the Center for Big Data in Economics and Finance . With 7 years of teaching and research experience since joining HSE in 2016, his work focuses on econometric methodologies and their applications in labor economics, financial modeling, and policy analysis. PhD in Economics (2020), HSE University Master's in Economics (2017), HSE University Bachelor's in Political Science (2015), HSE University Potanin's research interests center on sample selection models , endogenous switching models , Heckman models , and GARCH models . His academic contributions include methodological advancements in econometric modeling and empirical analyses of education returns, labor market phenomena, and financial volatility. His recent publications demonstrate expertise in multivariate econometric modeling , asymmetric risk premium analysis , and education-health linkages . Key journals include International Review of Financial Analysis , Journal of Economic Studies , and Applied Econometrics . Scientific Achievements: Best Teacher award (2024) Golden Tower Award (2019, Silver Chick nomination) HSE Personnel Reserve member Multiple publication bonuses (2017–2025) Potanin teaches Microeconometrics , Machine Learning in Economics , and Probability & Statistics courses at both undergraduate and graduate levels, with consultation hours held via Telegram.

• Spatial Econometrics
• Microeconometrics
• Nonparametric Estimation Methods
• +۳ مورد دیگر

Elena Vyacheslavovna Semerikova is an Associate Professor at the Department of Applied Economics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE). She also holds positions as Senior Research Fellow at the Scientific and Educational Laboratory of Spatial-Econometric Modeling of Socio-Economic Processes in Russia and as Leading Expert at the Project Laboratory for the Development of Intellectual Competitions in Economics. Dr. Semerikova has been working at HSE since 2010 and has accumulated 7 years of scientific and teaching experience. Education: 2017: Candidate of Economic Sciences 2013: Master's degree in Economics from National Research University Higher School of Economics 2011: Bachelor's degree in Economics from National Research University Higher School of Economics Dr. Semerikova specializes in spatial econometrics, microeconometrics, and nonparametric estimation methods. Her research primarily focuses on regional economic analysis, with particular emphasis on housing markets, labor markets, and spatial dependencies in economic phenomena. She has extensively studied regional disparities in Russia and Germany, examining topics such as housing affordability, unemployment patterns, and commuting flows through sophisticated spatial econometric techniques. Her work often involves panel data analysis and spatial regression models to uncover complex relationships between economic variables across geographical regions. Dr. Semerikova's publication record demonstrates a consistent focus on spatial econometric methodologies applied to real-world economic problems. Her recent work has increasingly addressed housing market dynamics in Germany, regional disparities in Russia, and the spatial patterns of labor markets. She frequently collaborates with international researchers, reflecting the global nature of her research interests and the recognition of her expertise in spatial econometrics. Awards and Recognition: Best Teacher award in 2024 and 2022 Letter of thanks from the Rector of HSE (July 2022) Gratitude from the Department of Applied Economics (January 2021) Member of the High Professional Potential Group (HSE personnel reserve) Category 'New teachers under 30' (2019–2020) Category 'Future teachers' (2015–2016) Oxford-Russia Foundation Scholar (2011-2012) Dr. Semerikova actively supervises graduate students, including advising A.S. Pilyuk on research related to housing affordability and migration. She has participated in numerous international conferences and workshops, including the Annual Meeting of the Armenian Economic Association, the Spatial Econometrics Advanced Institute (SEAI), and various workshops on spatial econometrics. She has also received research funding, including a Triple I grant for academic mobility with Humboldt University in Berlin. She is a key member of the Scientific and Educational Laboratory of Spatial-Econometric Modeling of Socio-Economic Processes in Russia, where she contributes to advancing methodological approaches in spatial econometrics and their application to pressing economic issues in Russia and internationally.

• Spatial Econometrics
• Bayesian Methods
• Microeconometrics
• +۵ 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.

• stochastic approximation
• reinforcement learning
• sampling
• +۴ مورد دیگر

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
• +۵ 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.