جست‌وجوی استاد و پژوهشگر

• convex optimization
• non-convex optimization
• stochastic optimization
• +۳ مورد دیگر

Nikita Evgenievich Yudin is a Research Fellow at the Laboratory of Theoretical Foundations of Artificial Intelligence Models within the Institute of Artificial Intelligence and Digital Sciences at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. He joined HSE University in November 2024 and works under the supervision of Puchkin N.A. 2020: Master's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University 2018: Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University April-July 2023: Research internship at MBZUAI in Abu Dhabi, United Arab Emirates Yudin's research focuses on advanced optimization techniques with applications in artificial intelligence. His primary areas include convex optimization, non-convex optimization, stochastic optimization, and reinforcement learning. His work centers on developing and analyzing optimization algorithms, particularly variants of the Gauss-Newton method, for solving complex mathematical problems in machine learning and decision processes. He applies these methods to Markov decision processes and reinforcement learning frameworks, contributing to theoretical foundations of AI models. His publication record shows a strong focus on optimization theory and its applications, with several papers published in reputable journals including Computational Mathematics and Mathematical Physics, Journal of Global Optimization, and Doklady Mathematics. His research demonstrates a progression from foundational work on nonlinear equation solving to more complex applications in Markov decision processes. Yudin maintains active scholarly profiles through ORCID (0000-0002-4505-7727), ResearcherID (LTF-7692-2024), Scopus (57222259323), and Google Scholar, indicating his commitment to academic visibility and collaboration. As a member of the Laboratory of Theoretical Foundations of Artificial Intelligence Models, Yudin contributes to HSE University's growing reputation in AI research. His work bridges theoretical mathematics with practical AI applications, focusing on the mathematical underpinnings that make advanced machine learning systems possible. His flexible office hours suggest an adaptable approach to collaboration within the academic community.

• Creativity
• Psychology of Thinking
• Methods of Creativity Development
• +۵ مورد دیگر

Sergey Rostislavovich Yagolkovsky is an Associate Professor at the Department of Psychology, Faculty of Social Sciences, National Research University Higher School of Economics (HSE), where he has been working since 2005. With 22 years of scientific and teaching experience, he has established himself as a leading expert in creativity research, psychology of thinking, and innovation studies. His academic journey spans multiple disciplines, reflecting his diverse educational background in psychology, natural sciences, and engineering. 2011: Academic title of Associate Professor 2005: Candidate of Psychological Sciences from Lomonosov Moscow State University 2001: MSc from University of Manchester 2001: Psychology degree from Moscow State University 1988: Engineering degree from Minsk Radiotechnical Institute Professor Yagolkovsky's research focuses on creativity, innovative thinking, and cognitive processes. His work explores functional fixedness, group creativity, and methods for developing creative abilities. He has developed approaches to stimulate creativity through semantic priming and naming objects, and has investigated cultural variations in creative evaluation. His research bridges psychology, cognitive science, and organizational behavior, examining how creativity manifests in educational, professional, and digital contexts. His extensive publication record shows a consistent focus on creativity assessment and enhancement, with recent work exploring AI applications in creativity evaluation and cross-cultural perspectives on creative thinking. His research demonstrates strong international collaboration, particularly with researchers from UAE, France, and Germany. Deputy Editor-in-Chief of the journal 'Psychology. Journal of the Higher School of Economics' since 2010 Senior Researcher at the Cognitive Research Laboratory (2011-2020) Recipient of multiple awards including 'Best Teacher' (2014-2020) and the 'Recognition Medal' for 15 years of service Professor Yagolkovsky actively contributes to academic life through conference participation and editorial work. His teaching covers creativity, innovative thinking, and psychology courses across multiple disciplines, reflecting his interdisciplinary approach. His research on functional fixedness has particular practical applications in educational and organizational settings seeking to enhance creative problem-solving abilities.

• Machine Learning
• Deep Learning
• Data Analysis
• +۳ مورد دیگر

Daniil Igorevich Burakov serves as a Research Intern at the Research and Educational Laboratory of Big Data Analysis Methods within the Institute of Artificial Intelligence and Digital Sciences, and as an Assistant in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE). He joined HSE in 2024 and holds an office at Pokrovsky Boulevard, 11, S801. His professional profile integrates academic teaching with active research in computational methods for healthcare applications. Education: Bachelor's degree in Software Engineering (2023), National Research University Higher School of Economics Burakov specializes in machine learning with emphasis on medical applications, evidenced by his publication on mortality prediction in cardiac patients. His teaching portfolio spans core computer science subjects including Machine Learning, Deep Learning, Data Analysis, and Computer Architecture across multiple degree programs. His research leverages KAN models and neural networks to solve clinical problems, bridging theoretical AI with practical healthcare outcomes. His single verified publication demonstrates focus on applying machine learning to cardiovascular medicine, indicating emerging expertise in medical AI. Burakov's work shows strong alignment with HSE's Institute of Artificial Intelligence and Digital Sciences priorities in healthcare analytics. Awards: Allowance for young teachers of the Faculty of Computer Science from among graduates (2024-2025) Burakov actively contributes to academic instruction while developing research in predictive healthcare analytics. His teaching responsibilities include foundational courses in Python programming, mathematical methods for data analysis, and specialized machine learning curricula for both Software Engineering and Information Security programs. He operates within HSE's Research and Educational Laboratory of Big Data Analysis Methods, which focuses on advanced computational techniques for complex data systems.

• exploratory behaviour
• complex problem solving
• complicology
• +۴ مورد دیگر

Alexander Poddiakov is a Tenured Professor at the National Research University Higher School of Economics (HSE University) in Moscow, where he has maintained faculty status since 2003. He holds dual appointments as a Leading Research Fellow at the International Laboratory for Logic, Linguistics and Formal Philosophy and as a Professor in the Faculty of Social Sciences/School of Psychology. His academic trajectory demonstrates consistent contributions across psychology, education, and mathematical reasoning, with particular expertise in complex cognitive systems and non-transitive relationships. Poddiakov earned his Candidate of Sciences (PhD equivalent) in Pedagogical Psychology from Lomonosov Moscow State University in 1990, followed by a Doctor of Sciences degree from the Russian Academy of Education Institute of Psychology in 2001. He was promoted to Professor in 2009 and achieved tenured status at HSE University in 2011, reflecting his substantial scholarly contributions. His educational background in psychology forms the foundation for his interdisciplinary research spanning cognitive science, educational theory, and formal systems analysis. Professor Poddiakov's research program centers on exploratory behavior, complex problem solving, and complicology—the systematic study of creating, diagnosing, and managing difficulties. His groundbreaking work on intransitivity of superiority relationships (where A beats B, B beats C, but C beats A) challenges conventional assumptions about rationality across multiple domains including biology, economics, and game theory. He has developed innovative methodological approaches using interactive exploratory objects to study how individuals navigate complex problem spaces characterized by uncertainty and conflicting information. His theoretical contributions extend to 'Trojan horse' teaching methodologies, evolutionary aspects of creating difficulties for others, and the psychological implications of non-transitive relationships in educational contexts. His publication record reveals a consistent intellectual trajectory examining how humans process complexity, uncertainty, and paradoxical situations. A defining theme across his work is the investigation of intransitive relationships and their implications for decision-making, educational practices, and cognitive development. His research spans theoretical foundations, experimental psychology, educational applications, and practical implications for understanding competitive dynamics in social and professional contexts, with increasing attention to digital environments and AI systems. Best Teacher Award at HSE University (2024, 2015, 2012) Best Monograph on Psychology Award at the All-Russian Competition of Psychological Books (2007) for "Psychology of competition in teaching/learning" Extensive editorial board service for journals including "Voprosy Psikhologii," "Integrative Psychological and Behavioral Science," and "Psychology. Journal of the Higher School of Economics" Professor Poddiakov actively supervises doctoral students, with particular focus on strategies for creating tasks that help and counteract learning in competitive gaming environments. His teaching portfolio is remarkably diverse, spanning Psychology, Sociology, Economics, Management, Law, History, and Mathematics programs across multiple HSE campuses. This interdisciplinary approach reflects his theoretical perspective that cognitive processes cannot be fully understood within narrow disciplinary boundaries. His courses, including "Psychology of Thinking and Reasoning" and "Psychology of Communication," emphasize practical applications of theoretical concepts in real-world contexts. As a Leading Research Fellow at the International Laboratory for Logic, Linguistics and Formal Philosophy, Poddiakov contributes to cutting-edge research at the intersection of formal systems and human cognition. His work with interactive exploratory objects represents an innovative methodology for studying cognitive processes through tangible, manipulable systems. Current research directions include examining the implications of intransitive relationships in AI systems, the psychology of knowledge sharing between researchers from rival nations, and the development of educational tools for teaching complex problem-solving skills in the digital age.

Konstantinova Olga Nikolaevnaمشاهده پروفایل

دانشیار

• Engineering Graphics
• Mineral Processing
• Computer-Aided Design (CAD)
• +۳ مورد دیگر

Konstantinova Olga Nikolaevna is an Associate Professor at the Department of Engineering Graphics within the Institute of Mining, Geology and Geotechnology at Siberian Federal University . With 38 years of professional experience since 1980, she specializes in Engineering Graphics , Mineral Processing , and Computer-Aided Design (CAD) . Education: Mining engineer-processor from Krasnoyarsk Institute of Non-Ferrous Metals (1980). Teaching: Descriptive geometry, engineering graphics, and computer graphics. Her research explores geometric modeling , mineral separation techniques , and CAD integration in education. Recent work focuses on projection drawing , design standardization , and educational technology for technical disciplines. She has co-authored textbooks on technical drawing and CAD applications , including electronic educational complexes. Her publications emphasize visualization , geometric modeling , and pedagogical innovation for mining and engineering students. Professional development includes training in information technologies (2010), project activities integration (2013), and educational project management (2017).

Protasova Galina Viktorovnaمشاهده پروفایل

مدرس ارشد

• Mining Engineering
• Engineering Graphics
• Descriptive Geometry
• +۳ مورد دیگر

Protasova Galina Viktorovna serves as Senior Lecturer in the Department of Engineering Graphics at Siberian Federal University's Institute of Mining, Geology and Geotechnology. Born in 1958 in Aban, Krasnoyarsk Krai, she graduated from the Krasnoyarsk Institute of Non-Ferrous Metals in 1980 with a mining engineer-mechanic qualification specializing in Mining Machines and Complexes. Her 24 scientific and educational-methodical works span mining engineering applications and modern graphical techniques, with 3 publications documented in the last 5 years prior to the scrape. Her formal education includes: Mining Engineer-Mechanic (1980), Krasnoyarsk Institute of Non-Ferrous Metals named after M.I. Kalinin, specializing in Mining Machines and Complexes Professional development encompasses: Financial Management (1999) Project Management in Educational Activities (2010) Intel 'Education for the Future' (2010) Descriptive Geometry Modernization (2012) Geometric and Computer Methods in Engineering Education (2013) Educational Project Management (2017) Protasova's research centers on engineering graphics pedagogy, integrating descriptive geometry with computer-aided design for mining and mechanical engineering applications. Her work emphasizes practical problem-solving through educational materials that bridge theoretical concepts and industrial requirements, particularly in spatial visualization and technical documentation standards. She focuses on enhancing students' graphical literacy through innovative teaching methodologies and curriculum development. Analysis of her 14 most recent publications (2001-2016) reveals consistent emphasis on educational resources for engineering graphics courses, with significant contributions to projection drawing techniques, sketching methodologies, and mining-specific applications like earthwork planning and geological structure representation. The works demonstrate evolving integration of computer graphics while maintaining foundational geometric principles. No scientific awards were documented in the source material. Professional activities include teaching Descriptive Geometry, Engineering Graphics, and Computer Graphics courses. While no student advisement or research grants are specified, her extensive publication record in educational methodology indicates significant curriculum development contributions. Her professional retraining in project management and educational technologies suggests active engagement in pedagogical innovation within her department. No laboratory or research team affiliations were mentioned in the provided text.

• federated learning
• Gradient-free algorithms
• machine learning
• +۱ مورد دیگر

Alexander Vladimirovich Lobanov serves as a Research Fellow at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. Affiliated with the Institute of Artificial Intelligence and Digital Sciences and the Laboratory of Theoretical Foundations of Artificial Intelligence Models since 2024, he operates under supervisor Puchkin N. A. from Pokrovsky Boulevard Building 11, Office T926. His research centers on federated learning architectures and gradient-free optimization algorithms, with deep specialization in convex optimization frameworks for machine learning systems. Lobanov develops novel black-box optimization techniques using kernel approximation methods to solve complex stochastic problems where gradient information is unavailable or computationally prohibitive. His 2024 publication in the Journal of Optimization Theory and Applications exemplifies his focus on zero-order accelerated stochastic methods, addressing critical challenges in derivative-free optimization. This work advances theoretical foundations for artificial intelligence systems requiring efficient computation in high-dimensional spaces. As an active member of the Laboratory of Theoretical Foundations of Artificial Intelligence Models, Lobanov collaborates on theoretical AI research with flexible scheduling. His professional identifiers include ORCID 0000-0003-1620-9581, ResearcherID IFR-8600-2023, and Scopus AuthorID 57219489070.

• Cognitive Psychology
• Cognitive Neuroscience
• Metacognition
• +۵ مورد دیگر

Timofey Aleksandrovich Berezner is a Lecturer and Junior Research Fellow at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Social Sciences and Department of Psychology. He serves as Deputy Head of the Department for student-related affairs and conducts research at the Laboratory for Cognitive Psychology of Digital Interface Users (HSE UX Lab). Since joining HSE in 2019, he has developed expertise in cognitive psychology, metacognition, and human-computer interaction. Education includes: Master's in Psychology (HSE, 2023) Bachelor's in Psychology (HSE, 2021) Professional retraining in Psychotherapy (2023-2024) Multiple professional development courses including Cognitive Science (Sofia, 2021), AI fundamentals (2024), and Schema Therapy (2023) Research focuses on: Cognitive processes in digital interfaces and learning environments Metacognitive regulation in decision-making and executive control Perceptual factors (disfluency effects, font psychology) AI-human cognition interactions His work bridges cognitive neuroscience with practical applications in UX design and education. Publication analysis reveals strong emphasis on: Metacognitive regulation mechanisms Perceptual influences on cognition Cognitive load measurement (NASA-TLX adaptation) Empirical studies of disfluency effects Human-AI interaction challenges Awards and Recognition: Best Teacher (2025) HSE High Professional Potential Group - New Researchers (2022-2023) Departmental commendation (2022) Winner of HSE Science Battles (2022) Professional activities include: Doctoral research on perceptual factors in metacognition Teaching cognitive psychology courses Membership in HSE UX Lab researching digital interface cognition External roles as AI-trainer at YandexGPT and lecturer at RANEPA

• Machine Learning
• Artificial Intelligence
• Big Data Analysis
• +۳ مورد دیگر

Artem Sergeevich Ryzhikov is an Associate Professor and Research Fellow at the National Research University Higher School of Economics (HSE), working within the Faculty of Computer Science. He is affiliated with both the Institute of Artificial Intelligence and Digital Sciences and the Department of Big Data and Information Retrieval, where he leads research in the Research and Educational Laboratory of Big Data Analysis Methods. 2024: Candidate of Physical and Mathematical Sciences, National Research University Higher School of Economics 2018: Master's degree in Applied Mathematics and Computer Science, National Research University Higher School of Economics 2015: Bachelor's degree in Physics, Novosibirsk National Research State University Dr. Ryzhikov's research spans the intersection of machine learning, artificial intelligence, and high-energy physics. His work focuses on developing and applying advanced computational methods to solve complex problems in particle physics, particularly in the areas of anomaly detection, detector calibration, and analysis of large-scale experimental data from facilities like LHCb. He has made significant contributions to amplitude analyses of B-meson decays, studies of CP violation, and applications of generative models in physics instrumentation. His interdisciplinary approach bridges theoretical physics with cutting-edge machine learning techniques, enabling novel insights into fundamental particle interactions and detector performance optimization. Analysis of Dr. Ryzhikov's recent publications reveals a strong focus on applying machine learning techniques to particle physics problems, particularly in detector calibration and data analysis. His work spans both theoretical developments in amplitude analysis and practical applications of deep learning for instrumentation challenges. The research demonstrates increasing integration of AI methods with traditional physics analysis techniques, reflecting broader trends in the field. Letter of thanks from the Vice-Rector of HSE (September 2022) Bonus for publication in international peer-reviewed scientific journal (2021-2022, 2022-2023) Bonus for regular publications in international peer-reviewed scientific journals (2024-2029) Dr. Ryzhikov supervises graduate students working at the intersection of computer science and physics. His research is supported by institutional funding from HSE and likely benefits from collaborations with major physics experiments like LHCb at CERN. He regularly publishes in top physics and computer science journals, demonstrating the impact and recognition of his interdisciplinary work. Dr. Ryzhikov leads research activities within the Research and Educational Laboratory of Big Data Analysis Methods, where his team develops novel computational approaches for physics data analysis. The laboratory serves as a hub for interdisciplinary collaboration between computer scientists and physicists, fostering innovation in both fields through the application of advanced data science techniques to fundamental research questions.

Fedor Aleksandrovich Buzaevمشاهده پروفایل

مدرس

• Physics-informed neural networks
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Fedor Aleksandrovich Buzaev is a Lecturer and Junior Research Fellow at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as a postgraduate student in his second year of study. He began his work at HSE in 2024 and is affiliated with both the Institute of Artificial Intelligence and Digital Sciences and the Department of Big Data and Information Retrieval. His educational background includes a Master's degree in Applied Mathematics and Computer Science from HSE (2023) and a Bachelor's degree in Electric Power Engineering and Electrical Engineering from MPEI (2020). Buzaev's research focuses on the intersection of physics and machine learning, particularly in developing physics-informed neural networks for solving complex scientific problems. His work spans several key areas including climate prediction, computational physics, and the solution of partial differential equations. He has made significant contributions to optimizing neural network architectures for scientific computing applications, with a particular emphasis on solving the nonlinear Schrödinger equation and other physics-based problems. His professional experience includes working as a deep learning researcher at Zvuk.com (since December 2024), a junior researcher at the Research and Educational Laboratory of Big Data Analysis Methods (since June 2023), and previous positions at Huawei RRI, JuniStat, and EdisonLab. He teaches the "Machine Learning 1" course at HSE and has expertise in multiple programming languages and research methodologies. Buzaev is currently working on his dissertation with the approved topic "Climate prediction using physically-informed neural networks" under the supervision of Fedor Dmitrievich Ratnikov. His research demonstrates a strong commitment to advancing the application of machine learning techniques in scientific domains, particularly where physical constraints and equations guide the learning process.

• Deep Learning
• AI Agents
• Anomaly Detection
• +۱ مورد دیگر

Alexander Ilyich Khizhik serves as a Junior Research Fellow at the Institute of Artificial Intelligence and Digital Sciences within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University), and also holds a position as Visiting Lecturer in the Department of Big Data and Information Retrieval. He joined HSE University in 2025. His educational background includes: 2022: Master's degree in Applied Mathematics and Computer Science from National Research University Higher School of Economics Khizhik's research focuses on cutting-edge areas of artificial intelligence and machine learning. His work spans deep learning architectures, AI agent development, anomaly detection systems, and practical machine learning applications. His research combines theoretical foundations with real-world implementations across various domains including electrical engineering and astrophysics. An analysis of his publications reveals expertise in applying neural network techniques to solve complex problems in both engineering systems and solar physics. His work demonstrates strong capabilities in developing hybrid approaches that integrate traditional methods with modern AI solutions, with particular attention to uncertainty quantification and practical implementation. He teaches "Generative Models in Machine Learning (Advanced Course)" for Bachelor's degree students across both the Faculty of Economic Sciences and the Faculty of Computer Science during the 2024/2025 academic year. Khizhik conducts his research work through the Research and Educational Laboratory of Big Data Analysis Methods, where he prepares materials for publication and collaborates with his supervisor Derkach D. A.

• 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.

Al-Maini Abdulaziz Rashid Khalidمشاهده پروفایل

پژوهشگر ارشد

• Computer Science
• Data Analysis
• Machine Learning
• +۳ مورد دیگر

Al-Maini Abdulaziz Rashid Khalid is a Junior Research Fellow at the Faculty of Computer Science, National Research University Higher School of Economics (HSE), working within the Institute of Artificial Intelligence and Digital Sciences and the Research and Educational Laboratory of Big Data Analysis Methods. He has been affiliated with HSE since 2020. His educational background includes a postgraduate study in Computer Science from HSE (2023) and a Master's degree in Informatics and Computer Engineering from the National University of Science and Technology "MISiS" (2020). Dr. Khalid's research focuses on interpretable generative models , computer science , and data analysis , with particular emphasis on applications in 2D materials science and performance modeling of data storage systems. His work bridges the gap between theoretical computer science and practical applications in materials science and data systems. His publications demonstrate a strong trend toward applying machine learning techniques to solve complex problems in materials science, particularly in analyzing defects in 2D materials like graphene and boron nitride. He has also contributed to the development of generative models for performance modeling of data storage systems. ORCID: 0000-0002-3299-7293 Researcher ID: HHC-1931-2022 As an educator, Dr. Khalid teaches "Machine Learning and Data Mining" for Master's students in the Faculty of Computer Science at HSE. His GitHub profile (abdalazizrashid) shows active contributions to open-source projects related to Bayesian modeling and marketing analytics, particularly in the PyMC ecosystem.