جست‌وجوی استاد و پژوهشگر

• Industrial Organization
• Competition Policy
• Antitrust Economics
• +۴ مورد دیگر

Yusupova Guzel Fatehovna 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), where she has been working since 1998 with 28 years of scientific and teaching experience. She also serves as a Senior Research Fellow at the Institute for Enterprise and Market Analysis, specifically in the Laboratory of Competition and Antimonopoly Policy. Her research interests focus on Industrial Organization, Competition Policy, Antitrust Economics, Digital Markets, Market Analysis, and Economics of Network Effects, with particular attention to Russian industrial markets. Her professional expertise stems from her Candidate of Economic Sciences degree (2007) in Economics and Management of the National Economy, with a dissertation on "Boundaries of Russian markets and competition," and her Associate Professor title awarded in 2013. Dr. Yusupova's publication record shows a consistent focus on competition policy and market analysis, with her most recent work examining AI applications in cartel detection, digital platform competition, and the nuances of antitrust enforcement in specialized markets. Her research demonstrates progression from foundational industrial organization topics to increasingly complex digital market challenges. Honorary certificate of the Ministry of Science and Higher Education of the Russian Federation (November 2022) Gratitude of HSE (March 2022) Gratitude of the Faculty of Economic Sciences HSE (January 2021) Winner of the Competition for the best Russian-language scientific works of HSE employees - 2021 Multiple academic bonuses for publications and contributions to HSE reputation (2010-2024) As an educator, Dr. Yusupova teaches graduate and undergraduate courses including Analysis of Industry Markets and Competition Policy, Introduction to the Economy of Digital Platforms, and Theory of Industrial Markets. Her teaching reflects her research expertise, bridging theoretical industrial organization concepts with practical antitrust applications. She has participated in numerous international conferences and research projects focused on competition policy effectiveness, with particular emphasis on Russian market contexts and transition economy challenges.

• Analysis and transformation of programs
• Software Security
• Parallel and distributed computing technologies

Harutyun Ishkhanovich Avetisyan is a Professor and Head of the Basic Department "System Programming" at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE). He began his tenure at HSE in 2017 and brings 30 years of scientific and teaching experience to his role. Additionally, he serves as the Director of the Institute for System Programming of the Russian Academy of Sciences (ISP RAS), a position he has held since 2015. Avetisyan holds numerous prestigious academic distinctions, including being elected as an Academician of the Russian Academy of Sciences in 2019 and as a Corresponding Member in 2016. He earned his Doctor of Physical and Mathematical Sciences degree in 2012 and was awarded the academic title of Associate Professor in 2009. His educational background includes a specialty in "Applied Mathematics" from Yerevan State University (1993). His research focuses on three main areas: analysis and transformation of programs, software security, and parallel and distributed computing technologies. These interests are reflected in his extensive publication record and leadership in major research initiatives. His work bridges theoretical computer science with practical applications in cloud computing, secure data storage, and high-performance computing systems. Avetisyan's scholarly contributions demonstrate a consistent focus on system programming challenges, particularly in the areas of code analysis, optimization, and security. His recent publications indicate a growing interest in cloud computing paradigms, smart city infrastructure, and energy-efficient computing solutions. His research has significant implications for both academic theory and industrial applications in software development. Among his notable recognitions, Avetisyan was awarded the medal of the Order "For Merit to the Fatherland" 2nd degree in 2021 for his significant contributions to science and dedicated service. He also serves on the editorial boards of several prestigious journals including "Programming" (since 2015) and "Proceedings of the Institute for System Programming of the RAS" (since 2010). Throughout his career, Avetisyan has led and participated in numerous research grants funded by the Ministry of Education and the Russian Foundation for Basic Research. His professional trajectory shows steady progression from postgraduate studies (1997-2000) to research fellow (2000-2002), deputy director of ISP RAS (2002-2015), and ultimately director of the institute (2015-present). At HSE, Avetisyan teaches courses in parallel programming and mentor seminars for master's students in Software Engineering. His teaching philosophy emphasizes the integration of cutting-edge research with practical software development skills, preparing students for careers at the forefront of computer science.

• Computer Vision
• Artificial Intelligence
• Pattern Recognition
• +۶ مورد دیگر

Andrey Vladimirovich Savchenko is a prominent researcher and educator in computer vision and artificial intelligence at the National Research University Higher School of Economics (HSE) in Nizhny Novgorod. He holds multiple positions including Professor at the Faculty of Informatics, Mathematics, and Computer Science, Leading Researcher at the Faculty of Computer Science and Institute of Artificial Intelligence and Digital Sciences, and Academic Director of the "Artificial Intelligence and Computer Vision" educational program. His educational background includes: 2016: Doctor of Technical Sciences from Nizhny Novgorod State Technical University 2015: Academic title of Associate Professor 2011: Candidate of Technical Sciences 2008: Specialist degree in Applied Mathematics and Computer Science Savchenko's research focuses on computer vision, pattern recognition, and artificial intelligence, with particular emphasis on facial recognition, emotion analysis, and efficient deep learning algorithms. His work bridges theoretical foundations with practical applications, especially in mobile computing environments where computational resources are limited. He has developed innovative methods for making AI systems more efficient without significant loss in accuracy. His recent publications demonstrate a strong trend toward multimodal analysis, combining visual, audio, and textual data for more robust recognition systems. There's a clear emphasis on making AI systems more efficient, especially for mobile devices, and on developing methods that can work with limited computational resources while maintaining high accuracy. His work spans fundamental research on neural network architectures and practical applications in education, healthcare, and human-computer interaction. Among his notable scientific achievements: Gratitude from the Governor of Nizhny Novgorod region (2022) Multiple gratitude awards from HSE (2021-2022) Best Teacher Award (2018-2019) Leaders of IT Industry Award from NEYMARK IT Campus (2023) Academic Success Bonus at HSE (2011-2013) Savchenko has successfully supervised numerous master's students and currently mentors PhD candidates working on cutting-edge topics like large language models for recommendation systems and document analysis. He has secured significant research funding, including projects with Huawei, Sberbank, and the Russian Science Foundation, totaling millions of rubles. His laboratory focuses on developing efficient algorithms for computer vision and multimodal data analysis. He leads the Laboratory of Theoretical Foundations of Artificial Intelligence Models and has established strong industry partnerships that ensure his research has practical impact. His NVIDIA Deep Learning Institute certification demonstrates his commitment to staying current with the latest AI technologies.

Rodriguez Zalepinos Ramon Antonioمشاهده پروفایل

دانشیار

• distributed systems
• databases
• Big data
• +۶ مورد دیگر

Dr. Ramon Antonio Rodriguez Zalepinos is an Associate Professor at the Department of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics (HSE). With 16+ years of scientific and teaching experience, he specializes in geospatial data systems, distributed databases, and high-performance computing. Doctor of Science in Computer Science (2024) Candidate of Technical Sciences (2013) Master's in Computer Science (2008, Donetsk National Technical University) His research focuses on geospatial array databases , distributed systems , and Big Earth Data engineering , particularly through his ChronosDB and Quantum Tensor DBMS projects. He has pioneered cloud-native solutions for multi-terabyte environmental datasets and developed novel approaches for in-database road traffic simulations. Key publication trends show 7+ years of contributions to VLDB and SIGMOD conferences, with special emphasis on: Quantum-enhanced geospatial processing Web-based array database systems Cellular automata integration High-speed raster data aggregation Scientific recognition includes: Best Teacher award (2017-2021, 2023) HSE Personnel Reserve member Additional post-doctoral funding (2024-2027) Multiple publication bonuses (2019-2023) He supervises student research in geospatial data science and leads projects on satellite data processing systems, with implementations at major institutions including Amazon and Planet Labs.

Shilov Valery Vladimirovichمشاهده پروفایل

استاد

• Parallel Computing
• Parallel Architectures
• History of Informatics
• +۳ مورد دیگر

Valery Vladimirovich Shilov is a Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University) in Moscow. He began working at HSE in 2015 and has extensive scientific and teaching experience spanning several decades across various prestigious institutions in Russia. His educational background includes a degree in Applied Mathematics from Lomonosov Moscow State University (1976), qualification as a Lawyer from the Higher Institute of Management (2001), and the academic title of Candidate of Technical Sciences (1989). His professional journey began at Central Research Institute 45 of the Ministry of Defense of the USSR (1976-1989), continued at the USSR Academy of Sciences Computing Center (1989-1997), and included professorial positions at MATI - Russian State Technological University named after K.E. Tsiolkovsky (1997-2015), where he also served as Head of the Department of Design of Computing Complexes and Deputy Dean of the Engineering and Economic Faculty for Science. Professor Shilov's research focuses on parallel computing, parallel architectures, and the history of informatics, with particular emphasis on Soviet and Russian computing history. His scholarly output demonstrates deep engagement with historical aspects of computing technology, including early mechanical calculators, Soviet-era computer development, and the evolution of computing in Eastern Europe. His publications reveal a consistent pattern of examining how computing technologies evolved within specific historical, political, and economic contexts, with special attention to Russian and Soviet contributions. His numerous scientific awards reflect significant recognition in his field, including honors from the Ministry of Education and Science of Russia, the Russian Section of IEEE Computer Society, the Polytechnic Museum, and multiple awards from HSE University. His leadership in organizing academic conferences, particularly those focused on the history of computing in Russia and former Soviet states, has been instrumental in preserving and advancing knowledge in this specialized area. Professor Shilov has supervised multiple PhD students focusing on software engineering, processor architecture, and parallel computing algorithms. His grant activities include leading an RFBR project on the history of domestic computing and informatics (1946-1960), demonstrating his commitment to documenting and analyzing the historical development of computing in Russia. He remains actively involved in academic service as a member of editorial boards for several journals and as a participant in numerous international conferences.

• Econometrics
• Macroeconometrics
• Microeconometrics
• +۷ مورد دیگر

Grigory Gelmutovich Kantorovich is a distinguished Research Professor at the National Research University Higher School of Economics (HSE), where he has been working since 1993. He serves as Head of the Scientific and Educational Laboratory of Macrostructural Modeling of the Russian Economy within the Faculty of Economic Sciences. He holds a tenured professorship since 2018 and has been recognized as a Distinguished Professor by HSE. With 54 years of scientific and teaching experience, Kantorovich has made significant contributions to the field of econometrics and economic modeling. His educational background includes a Candidate of Physical and Mathematical Sciences degree (1974) from Moscow Institute of Physics and Technology, where he specialized in Automatic Regulation and Control Theory. He graduated with honors from the same institution in 1971 with a degree in Flight Dynamics and Motion Control of Aircraft. His extensive professional development includes numerous international programs at Erasmus University Rotterdam, University of Paris 1 - Sorbonne, Harvard University, and the London School of Economics. Kantorovich's research spans multiple areas of econometrics, including macroeconometrics, microeconometrics, financial econometrics, and time series modeling. His work focuses on dynamic modeling of economic development, macrostructural modeling of the economy, and econometric modeling of socio-economic processes. His publications demonstrate a consistent focus on applying rigorous statistical methods to economic problems, with particular attention to risk evaluation in financial markets, structural shifts in time series, and the impact of investments on economic efficiency. Throughout his career, Kantorovich has received numerous prestigious awards including the Medal of the Order "For Merits to the Fatherland" (both I and II degrees), multiple Honorary Certificates from HSE, and recognition as Best Teacher in multiple academic years. He was awarded the "Golden HSE" Award twice for his contributions to teaching and school development. As an academic advisor, Kantorovich has supervised doctoral research and mentored students including Lana Zakharova (on economic crises determinants), Nikolai Shugal (on gross value added modeling), and Elena Nazrullaeva (on capital investments impact). He has also participated in numerous international research projects, including a World Bank project on energy price impacts and grants from the MacArthur Foundation. He leads the Scientific and Educational Laboratory of Macrostructural Modeling of the Russian Economy, where he conducts research on economic modeling and advises on policy-related economic issues. His work bridges theoretical econometrics with practical applications in Russian economic policy and education.

• convex optimization
• optimal transport
• distributed optimization
• +۱ مورد دیگر

Dvinskikh Darina Mikhailovna serves as both Associate Professor and Senior Research Fellow at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science, the Institute of Artificial Intelligence and Digital Sciences, and the Department of Big Data and Information Retrieval. She earned her PhD from Humboldt University of Berlin in 2021, following Master's (2018) and Bachelor's (2016) degrees in Applied Mathematics and Physics from Moscow Institute of Physics and Technology. Prior to joining HSE in 2022, she conducted research at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin (2018-2021). Dr. Dvinskikh specializes in convex optimization, optimal transport, distributed optimization, and numerical optimization methods. Her research bridges theoretical foundations with practical applications in machine learning and artificial intelligence, with particular expertise in zeroth-order optimization methods, stochastic optimization frameworks, and decentralized computing approaches. Her work has advanced the field through novel algorithms for black-box optimization and improved complexity bounds for Wasserstein barycenter problems. Her publications span top venues including NeurIPS, Optimization Methods and Software, and Computational Mathematics and Mathematical Physics, demonstrating her contributions to both theoretical and applied aspects of optimization. She has received institutional recognition including a Letter of gratitude from the First Vice-Rector of HSE (May 2024) and a publication bonus for work in List A journals (2023-2024). Dr. Dvinskikh teaches Mathematical Statistics 1 and Modern Algorithmical Optimization for undergraduate and graduate students, emphasizing both theoretical understanding and practical implementation of optimization algorithms. Her teaching philosophy aims to provide students with a comprehensive understanding of how optimization techniques power modern machine learning systems.

• Intelligent Transport Systems
• Location-Based Services
• Software and Hardware Integrated Navigation Systems
• +۷ مورد دیگر

Saleh Hadi Mohammed is an Associate Professor at the Faculty of Computer Science , National Research University Higher School of Economics (HSE), where he has worked since 2015. With over 10 years of academic experience, his expertise spans software engineering, blockchain systems, and intelligent transport technologies. Candidate of Technical Sciences (2013) Engineer in 'Computers, Complexes, Systems and Networks' (2008) Professional training in Moodle-based distance learning Research interests include: Intelligent transport systems and location-based services Software/hardware navigation integration and blockchain applications Cloud services, mobile app development, and MLOps platforms His recent publications analyze MLOps scalability, blockchain donation tracking, and AI-driven navigation for visually impaired users. Notable collaborations involve Springer, IEEE, and CEUR Workshop Proceedings. Scientific awards : Gratitude from HSE Faculty of Computer Science (2024) As a thesis supervisor , he has guided 15+ students in projects ranging from fraud detection to mobile education apps. He also leads SmartMLOps , HSE’s AI service deployment platform (2024), and participated in the Administrative Personnel Reserve Program (2024-2025).

• 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.

• Computational Biology
• Bioinformatics
• Mass Spectrometry Data Analysis
• +۵ مورد دیگر

Attila Kertesz-Farkas is a Hungarian-born academic currently serving as Assistant Professor at the Faculty of Computer Science, Department of Data Analysis and Artificial Intelligence, at the National Research University Higher School of Economics (HSE) in Moscow. Since 2021, he has also been Head of the Research and Educational Laboratory of Artificial Intelligence for Computational Biology. He joined HSE in 2015 after completing postdoctoral positions at the University of Washington and the International Centre of Genetic Engineering and Biotechnology. His educational background includes: Doctor of Science (2022) from National Research University Higher School of Economics PhD (2010) from University of Szeged Master's degree in Computer Science (2004) from University of Szeged Kertesz-Farkas's research focuses on the intersection of artificial intelligence and computational biology, particularly in mass spectrometry data analysis. His work spans computational proteomics, human gait analysis, and medical applications of machine learning. He develops novel algorithms for peptide identification, protein classification, and single-cell analysis, with applications ranging from cancer research to environmental contamination analysis. His research group actively explores how deep learning can improve the analysis of biological data that is inherently non-human readable. His scientific contributions have been recognized with several awards including a Letter of thanks from the Rector of HSE (December 2022), a Letter of gratitude from the Faculty of Computer Science (September 2021), and the Best presentation award at ICMLC 2023 conference. As an academic supervisor, Kertesz-Farkas mentors multiple PhD students working on diverse projects including generative models for mass spectrometry data, human gait control systems for prosthetics, and deep learning applications for single-cell analysis. His research is supported by the allowance for defending a doctoral dissertation (2022-2025) and various research projects at HSE. He leads the Laboratory of Artificial Intelligence for Computational Biology, which focuses on developing AI methods specifically tailored for biological data analysis problems. The laboratory conducts research in computational proteomics, medical applications of machine learning, and the development of tools for mass spectrometry data interpretation.

• marketing communications
• marketing research
• communication activity
• +۳ مورد دیگر

Ekaterina Artyushina serves as an Associate Professor at the Higher School of Economics (HSE University) at the Nizhny Novgorod campus, where she is affiliated with the Faculty of Management and Department of Marketing. She has been with HSE University since 2016 and serves as the Academic Supervisor for the Digital Marketing program. Her academic credentials include Candidate of Sciences (PhD equivalent) earned in 2007 and an undergraduate degree in Business Economics and Management from Nizhny Novgorod State University of Architecture and Engineering. 2011: Associate Professor title awarded 2007: Candidate of Sciences (PhD equivalent) from Nizhny Novgorod State University of Architecture and Engineering 1999: Degree in Business Economics and Management from Nizhny Novgorod State University of Architecture and Engineering Professor Artyushina's research focuses on contemporary marketing challenges in the digital age, with particular expertise in marketing communications, marketing research, and masstige marketing strategies. Her work examines how digital transformation affects consumer behavior, brand communication, and marketing channel effectiveness. She has published extensively on topics including digital communication channels for cosmetic brands, user experience on internet platforms, and the application of masstige strategies in the Russian market. Her recent publications (2020-2025) reveal a clear research trajectory focused on digital marketing transformations, with increasing attention to platform economics, EdTech branding, and pandemic-era business adaptations. The publications demonstrate methodological diversity, combining empirical consumer research with strategic marketing analysis, particularly examining how traditional marketing concepts adapt to digital environments. Professor Artyushina teaches multiple marketing courses across HSE University's programs, including Marketing, Marketing Communications, Marketing Research, and Marketing: Integration, Interaction, Promotion. Her teaching spans bachelor's, master's, and minor programs, reflecting her ability to adapt marketing education for different academic levels and disciplines. She has consistently taught these courses from 2020-2025 across multiple academic terms, demonstrating her commitment to marketing education at HSE University.

• Diagnostic Features
• Testing Electronic Means
• Charging of Polymers
• +۳ مورد دیگر

Andrey E. Abrameshin is a Professor and Deputy Director at the HSE Tikhonov Moscow Institute of Electronics and Mathematics , affiliated with the School of Electronic Engineering . He has been at HSE University since 2012, contributing to research in spacecraft charging, polymer conductivity, and aerospace material reliability. His academic background includes a Candidate of Sciences (PhD) in Sociological Sciences (1997) and a Degree from Moscow Institute of Electronic Engineering (1986). Education: Candidate of Sciences (PhD) in Sociological Sciences (1997) Degree from Moscow Institute of Electronic Engineering (1986) Research Interests His work focuses on radiation-induced conductivity in polymers, spacecraft internal charging , low-energy electron effects , and dielectric material reliability . He explores charge transport mechanisms, ESD mitigation, and macro-modeling for spacecraft systems. Recent Publications Trends His 2013–2022 articles emphasize polymer conductivity under radiation , spacecraft material simulation , and macro-modeling of electrical systems . Key subtopics include dielectric composites , electron trapping , charge distribution , and radiation-resistant electronics for aerospace applications. Labs & Projects Abrameshin leads research at the Laboratory of Space Vehicles and Systems' Functional Safety , collaborating on satellite development (e.g., CubeSX-HSE) and spacecraft electronics reliability. He contributed to Russia's spacecraft charging studies and participated in international conferences like Spacecraft Charging Technology Conference.

• Petri nets
• Multi-agent systems
• Distributed systems
• +۴ مورد دیگر

Roman Aleksandrovich Nesterov is an Associate Professor at the Department of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics (HSE). He has been with HSE since 2015 and heads the Research and Educational Laboratory of Process-Oriented Information Systems (POIS). His educational background includes a PhD (2022) and Master's degree (2017) from HSE, both in Computer Science and Software Engineering. His research focuses on: Formal methods for distributed system modeling Petri nets and concurrency theory Process mining and event log analysis Multi-agent systems and business process modeling Compositional approaches to system verification His publications demonstrate consistent focus on process discovery algorithms, model verification techniques, and applications of Petri nets in distributed systems. Recent works emphasize hierarchical modeling and architecture-aware solutions for multi-agent environments. Awards and Honors: Golden HSE Prize 2019 (Silver Chick nomination) Multiple 'Best Teacher' awards (2020-2025) Research publication bonuses HSE Faculty of Computer Science Gratitude (2023) He leads the POIS laboratory where he conducts research on automated synthesis of multi-agent system models. His teaching includes undergraduate algorithms courses and graduate-level formal methods in software engineering.

Khritankov Anton Sergeevichمشاهده پروفایل

دانشیار

• Machine Learning
• Software Engineering
• MLOps
• +۳ مورد دیگر

Khritankov Anton Sergeevich is an Associate Professor at the Faculty of Computer Science , National Research University Higher School of Economics (HSE) , where he started working in 2021. He holds a Candidate of Physical and Mathematical Sciences (2010) and a Master's degree (2007) and Bachelor's degree (2005) in Applied Mathematics and Physics from the Moscow Institute of Physics and Mathematics (State University) . Research Interests include machine learning , software engineering , MLOps , metamorphic testing , and feedback loops in ML systems . His work focuses on automating and ensuring reproducibility in data science, with a particular emphasis on blockchain verification and hidden feedback mechanisms that cause concept drift in AI systems. Publication Trends reveal a strong focus on MLOps platforms (2025), metamorphic testing for AI/ML systems (2024–2022), blockchain verification (2022–2021), and feedback loop modeling (2021–2020). His recent work addresses challenges in distributed computing and state registration of software developed with the AI Center project teams at HSE. Scientific Awards include a Bonus for publication in a List A journal (2024–2025) . Additional Contributions involve developing the SmartMLOps platform (2024–2025), co-authoring state-registered software frameworks (2024), and organizing roundtable discussions on reproducibility in data science (2021). He has taught Advanced Software Design to Master's students in Software Engineering since 2021.

• medical informatics
• image analysis and processing
• medical information systems
• +۶ مورد دیگر

Oleg Stanislavovich P'yanykh is a Professor in the Department of Data Analysis and Artificial Intelligence at the Faculty of Computer Science, National Research University Higher School of Economics (HSE) in Moscow. He has been working at HSE since 2012 with 13 years of scientific and teaching experience. P'yanykh maintains active roles as a Guest Editor for both the American Journal of Roentgenology (since 2011) and Pattern Recognition (since 2010), and serves as a permanent member of the International DICOM Committee working group. His educational background includes a PhD in Computer Science from Louisiana State University (1998), an MS with honors in Applied Mathematics and Physics from Moscow State University (1994), and a Diplôme d'Etudes et de Recherches in Philosophy from French University College, Moscow State University and Sorbonne University (1994). P'yanykh's research spans medical informatics, image analysis and processing, medical information systems and standards (particularly DICOM and PACS), computer-aided diagnostics, teleradiology, machine learning, and control theory. His work bridges computer science with practical medical applications, focusing on improving medical imaging systems and data analysis. His publication record shows consistent contributions from 1997 through 2024, with recent work emphasizing scheduling algorithms, machine learning interpretability, human knowledge modeling, and medical image quality assessment. His research demonstrates a clear trajectory from foundational signal processing work to increasingly applied medical informatics solutions. Bonus for publication in an international peer-reviewed scientific journal (2019-2021) Bonus for publication in an international peer-reviewed scientific journal (2017-2018) Bonus for an article in a foreign peer-reviewed journal (2013-2015) P'yanykh has supervised student research including Viktor Sergeevich Lopatin's bachelor's thesis on 3D medical image processing and Ksenia Dmitrievna Loginova's master's thesis on medical image quality assessment. He has taught courses including Big Data and Machine Learning in Healthcare across multiple academic years (2020-2026) and Medical Informatics (2020-2022), reflecting his focus on applying computational methods to healthcare challenges.