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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.

• Computer Vision
• Artificial Intelligence
• Pattern Recognition
• +6 more

Alexander Alexandrovich Kharlamov serves as a Professor at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science and Department of Software Engineering. He joined HSE in 2015 and has accumulated 42 years of scientific and teaching experience. His academic profile includes prominent identifiers such as ORCID: 0000-0003-2942-5101, ResearcherID: E-3760-2014, and Scopus AuthorID: 57193909855. He maintains an office at the Pokrovsky Boulevard campus (AUK "Pokrovsky Boulevard", Pokrovsky blvd, 11, office S913) and serves as the Editor-in-chief of the journal "Speech Technologies" since 2008. Doctor of Technical Sciences (2010) from Moscow State Institute of Electronics and Mathematics Candidate of Technical Sciences (1983) from Moscow State Technical University named after N.E. Bauman Postgraduate study (1980) at Moscow State Technical University named after N.E. Bauman Mathematics degree (1976) from Lomonosov Moscow State University Thermal Physics degree (1970) from Moscow Power Engineering Institute Professor Kharlamov's research focuses primarily on neuroinformatics, with significant contributions to neural networks, semantic representations, and intelligent information systems. His work bridges computational approaches with cognitive modeling, exploring how semantic networks can be used for text analysis, speech recognition, and situation monitoring. His research interests span both theoretical foundations of neuroinformatics and practical applications in areas such as social media analysis, smart city development, and digital transformation. He has developed the TextAnalyst technology for automatic semantic analysis of text and has made significant contributions to understanding how humans process information through neural network models. His recent scholarly output demonstrates a clear trajectory toward multimodal systems, digital transformation in urban environments, and advanced semantic analysis techniques. The publications reveal a consistent focus on applying neuroinformatics principles to real-world problems, particularly in analyzing social dynamics through digital footprints, developing intelligent monitoring systems, and creating semantic frameworks for information processing. His work increasingly addresses contemporary challenges such as pandemic response, social stress analysis, and digital conflict zones, showing adaptability to emerging societal needs while maintaining theoretical rigor. Corresponding Member of the International Academy of Informatization (1994) Professor Kharlamov has secured significant research funding through grants from the Russian Foundation for Basic Research (No. 14-06-00363) and the Russian Humanitarian Science Foundation (No. 15-03-00860). His research leadership spans decades, with documented projects from 1996 to present, covering areas such as neural network modeling, speech recognition systems, semantic analysis technologies, and intelligent information systems. He has served as principal investigator for numerous projects, often collaborating with institutions like IITP RAS and Bauman Moscow State Technical University. His teaching portfolio includes advanced courses in neuroinformatics, neuromathematics, and neural network theory for both bachelor's and minor programs at HSE. As Editor-in-chief of the journal "Speech Technologies" since 2008, Professor Kharlamov has shaped discourse in his field while continuing his own research on semantic networks and neural modeling. His work on homogeneous semantic networks represents a significant contribution to the field, providing frameworks for text analysis, situation monitoring, and knowledge representation. The integration of his theoretical work on neural networks with practical applications in information systems demonstrates a career-long commitment to bridging theoretical computer science with real-world implementation challenges.

• Neuroinformatics
• Neural Networks
• Semantic Networks
• +6 more

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).

• Intelligent Transport Systems
• Location-Based Services
• Software and Hardware Integrated Navigation Systems
• +7 more

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.

• Computational Biology
• Bioinformatics
• Mass Spectrometry Data Analysis
• +5 more

Sergey Lvovich Makarov serves as an Associate Professor in the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE), where he has been employed since 2012. With 21 years of scientific and teaching experience, he holds formal academic credentials including a Candidate of Technical Sciences degree (awarded in 2009) from the Moscow State Institute of Electronics and Mathematics, specializing in Automated Design Systems. His research interests span software engineering, artificial intelligence, mobile development, Internet of Things technologies, automatic text processing, and an unusual interdisciplinary focus on fashion informatics where he applies computational methods to analyze symbolic systems in costume design. This dual expertise is reflected in his publications that bridge technical computer science with applications in fashion industry analysis. Makarov's scholarly output demonstrates a consistent trajectory from foundational work in intelligent information processing systems toward increasingly sophisticated applications in mobile development, IoT ecosystems, and fashion data analysis. His recent publications (2018-2023) focus heavily on practical hardware-software integration through Arduino and Raspberry Pi platforms, while maintaining his distinctive research thread analyzing symbolic systems in fashion design through computational methods. Gratitude from HSE University (May 2024) Gratitude from the Department of Software Engineering, HSE (November 2023) Medal 'Recognition - 10 years of successful work' (December 2022) Gratitude from the Faculty of Computer Science, HSE (September 2019) Academic Work Allowance (2014-2018) As an educator, Makarov supervises student research including Potapenko Vladimir Andreevich's 2013 qualification work on CRM system development. He has received multiple Academic Work Allowances recognizing his teaching excellence. His research includes significant grant-funded projects, notably serving as responsible executor for the R&D project 'Intelligent system for searching the Internet for information analogues of text documents' (2010-2011) and participating in research on decision support systems (2012). His scholarly contributions include numerous publications, research grants, and participation in conferences focusing on AI applications and intelligent systems. Makarov maintains active involvement in curriculum development, particularly in mobile application development and IoT technologies, as evidenced by his current teaching assignments for the 2025/2026 academic year which include specialized courses in Android development using both Kotlin and Java, Manufacturing Data Collection and Analytics, and IoT Ecosystems.

• Software Engineering
• Artificial Intelligence
• Mobile Development
• +6 more

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.

• medical informatics
• image analysis and processing
• medical information systems
• +6 more

Dmitry Igorevich Ryabtsev is an Associate Professor at the Faculty of Computer Science, Department of Software Engineering at the National Research University Higher School of Economics (HSE), where he began working in 2024. With a unique interdisciplinary background bridging medicine and computer science, he brings over a decade of medical experience to his academic role. His educational background includes a Master's degree in Software Engineering from HSE (2024), Candidate of Medical Sciences (2014), and a Doctor qualification in General Medicine from Stavropol State Medical University (2007). Prior to joining HSE, he worked as an ophthalmologist at various institutions including the Helmholtz Research Institute of Eye Diseases, Bascom Palmer Eye Institute in the USA, and Russian Railways medical services. Ryabtsev's research bridges computer science and medical applications, with particular focus on data analysis in biology and medicine, artificial intelligence in medicine, medical imaging, and medical information systems. His scholarly work demonstrates a consistent integration of medical expertise with computational approaches, as evidenced by his publications spanning ophthalmology, medical imaging, and computer vision applications in healthcare. His recent publications show a clear trajectory toward applying computer vision and AI to medical problems, culminating in his 2025 preprint on CV systems for medical applications. This represents a natural evolution from his earlier clinical research in ophthalmology to computational approaches in medicine. As an educator, he teaches courses including Basics of C++ programming, Fundamentals of Medical Systems Design, and Python programming language to undergraduate students in Software Engineering and Applied Mathematics programs. His teaching reflects his interdisciplinary expertise, connecting software engineering principles with medical applications. Ryabtsev is actively involved in research projects connecting computer science with medicine, including the EYAS system (for which he co-authored a 2025 software patent) and has served as a mentor for the 'Big Challenges' program at the Sirius educational center, where students developed AI-based medical devices.

• data analysis in biology and medicine
• innovations in medicine
• integration of medical systems
• +6 more

Sergey Shershakov is an Associate Professor at the Faculty of Computer Science, Department of Software Engineering at the National Research University Higher School of Economics (HSE University). He serves as the Academic Director of the Systems and Software Engineering educational program and has been with HSE University since 2010. His academic journey includes a PhD from HSE University (2020) and a Master's degree in Software Engineering from the same institution (2012). Dr. Shershakov's research focuses primarily on Process Mining and Software Engineering, with specific interests in: Process Mining methodologies and tools Software Process Mining and analysis Object-Oriented and Component-Oriented Programming Information Systems Architecture Verification of Algorithms Embedded Systems development His scholarly contributions demonstrate a consistent focus on developing practical tools and methodologies for process mining, with particular emphasis on creating graphical interfaces (like VTMine for Visio), improving performance of process mining algorithms, and applying these techniques to real-world systems. His work bridges theoretical computer science with practical software engineering applications, especially in the domain of business process analysis and optimization. Dr. Shershakov has received numerous recognitions for his academic work: Best Teacher award (2014, 2017-2021) Best Academic Supervisor in the category 'Admission of Foreign Students' (2024) Multiple Gratitude awards from HSE University leadership (2017-2024) Academic Work Allowance (2016-2021) Member of the 'New Researchers' category in the High Professional Potential Group (2014-2015) As an educator, Dr. Shershakov teaches multiple courses in C++ programming, Algorithms and Data Structures, and Introduction to Programming across different departments at HSE University. He leads research projects including an RFBR grant focused on 'Development of methods for efficient implementation of algorithms of process mining based on large event logs' (2018). He is also an active member of the IEEE Task Force on Process Mining. Dr. Shershakov is affiliated with the Laboratory of Process-Aware Information Systems (PAIS Lab) at HSE University, where he conducts research on process mining techniques and their applications. His work has significant implications for business process optimization, system verification, and software engineering practices.

• Process Mining
• Software Process Mining
• Object-Oriented Programming
• +7 more

Sergey Aleksandrovich Videnin is an 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) since 2022. His academic background includes a Candidate of Pedagogical Sciences degree (2010) and extensive professional experience in both academia and industry. Prior to joining HSE, he served as Deputy Director of the IT Education Development Center at Moscow Institute of Physics and Technology (2021-2022) and as Head of the Department of Information Systems at Siberian Federal University (2010-2021). Videnin's educational background includes graduation from Krasnoyarsk State Pedagogical University named after V.P. Astafyev in 2005 with a specialty in "Informatics with an additional specialty in pedagogy-psychology" and qualification as a "Teacher-psychologist." He has completed numerous professional development courses in educational technologies, digital transformation of education, and software engineering from HSE University, Siberian Federal University, and other institutions between 2011 and 2025. His research interests span information systems architecture, design patterns, object-oriented programming theory, theoretical foundations of programming, programming languages, and application software. Videnin has made significant contributions to educational technology, particularly in implementing active learning methods in programming education within the CDIO (Conceive-Design-Implement-Operate) engineering education framework. Videnin's recent publications demonstrate a strong focus on the intersection of software engineering principles and educational practices, with particular attention to practical applications in real-world contexts including real estate market analysis, biological image processing, and industrial process control. His work shows a consistent trend toward developing practical solutions that bridge theoretical concepts with tangible applications across diverse domains. Gratitude from the Department of Software Engineering, HSE (November 2023) Best Teacher - 2023-2025 High professional potential group (HSE personnel reserve) Category "New teachers" (2023) Winner of the All-Russian competition "League of Lecturers" organized by the Russian society "Knowledge" (May 2023) Videnin has been actively involved in teaching various computer science courses including Software Design, Computer System Architecture, Operating Systems, and Java Programming. He has participated in significant research projects funded by RFBR, the Ministry of Education RF, and RSF, focusing on information systems architecture, digital transformation of education, and industrial process control. His work demonstrates a strong commitment to both theoretical research and practical applications in software engineering and educational technology.

• Information Systems Architecture
• Design Patterns
• Object-Oriented Programming
• +5 more

Evgeny Belyaev is a Research Fellow at ITMO University's International Laboratory 'Computer Technologies'. Holding dual degrees as Candidate of Science in technical sciences (Russia, 2009) and Doctor of Technology (Tampere University of Technology, Finland), he specializes in video coding, drone-based infrastructure inspections, and vehicular communication systems. Education: Candidate of Science (Technical Sciences, 2009, Russia), Doctor of Technology (Tampere University of Technology, Finland) His research focuses on video coding , drone inspections , and real-time transmission systems , with applications in road surveillance and infrastructure monitoring. Key contributions include iterative aerial map construction techniques and robust video transmission protocols. Scientific achievements include the Grand Video Compression Challenge award (2013), multiple IEEE Exemplary Reviewer recognitions (2013, 2015, 2016), and a Best Paper Award (2011). He has authored over 40 publications and holds 4 patents.

• Computer Science
• Video Coding
• Drone Technology
• +5 more

Valeria Orlanovna Kirova is a Research Fellow at the National Research University Higher School of Economics (HSE University), working 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 joined HSE in 2020 and holds a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2024), following her Master's (2019) and Bachelor's (2017) degrees from National Research Tomsk State University. Her educational background includes: Candidate of Physical and Mathematical Sciences (PhD equivalent), Lomonosov Moscow State University, 2024 Master's degree in Mathematics, National Research Tomsk State University, 2019 Bachelor's degree in Mathematics and Computer Science, National Research Tomsk State University, 2017 Dr. Kirova specializes in discrete mathematics and combinatorics, with particular expertise in combinatorics of symbolic sequences, combinatorial geometry, and applications of these mathematical frameworks to artificial intelligence and biological sequence analysis. Her research bridges theoretical mathematics with practical applications, especially in neuroscience and hydrobiology. She teaches Mathematics for Data Analysis at both undergraduate and graduate levels at HSE University. Her publication record shows a strong focus on combinatorial complexity functions, symbolic sequences, and geometric properties of discrete structures. Her recent work demonstrates increasing interdisciplinary applications, particularly in AI methods for neuroscience (as part of the 'Next-Generation Cognitive AI' grant project) and hydrobiology (for biodiversity analysis and zooplankton classification using interpretable computer vision). Dr. Kirova has presented her research at numerous national and international conferences, including the IV Conference of Russian Mathematical Centers (2024), Numeration 2023 conference at University of Liège, and the International Lomonosov Conference (2023). She has also lectured at scientific schools on modern combinatorics and AI. She has been actively involved in research collaborations, serving as a Research-engineer at HUAWEI Russian Research Center (December 2021-April 2024), where she analyzed state-of-the-art approaches for photo and video processing. Additionally, she serves as an expert for the working group developing the Federal Law 'On the Regulation of Artificial Intelligence Systems in the Russian Federation'.

• Discrete Mathematics
• Combinatorics
• Combinatorics on Words
• +4 more

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
• +2 more

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
• +2 more

Ryabtsev Dmitry Igorevich is an Associate Professor at the Faculty of Computer Science, National Research University Higher School of Economics (HSE), starting in 2024. With 10 months of scientific and teaching experience at HSE, he bridges computer science and medical disciplines through courses like 'C++ Programming' (09.03.04 Software Engineering) and 'Fundamentals of Medical Systems Design.' Master's Degree in Software Engineering (2024, HSE) Candidate of Medical Sciences (2014, Russian Federation Ministry of Education and Science) Doctor in General Medicine (2007, Stavropol State Medical University) His research integrates artificial intelligence , medical imaging , and data analysis to advance telemedicine and medical information systems . Recent publications focus on computer vision applications in healthcare and retinal pathology diagnostics for premature infants. He co-developed the Web client of the EYAS system (2025) and participates in flagship programs like 'Big Challenges' at Sirius Educational Center. Ryabtsev collaborates with institutions such as the Bascom Palmer Eye Institute and the National Medical Research Center 'Microsurgery of the Eye.'

• data analysis in biology and medicine
• innovations in medicine
• integration of medical systems
• +6 more

Ilya Yuryevich Samonenko serves as Associate Professor at the Department of Big Data and Information Retrieval within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University). He holds dual administrative roles as Deputy Dean for Academic and Methodological Work for the Faculty of Computer Science and Director of the Center for organizing student training for top specialists in the field of artificial intelligence. Samonenko joined HSE University in 2018 and brings 11 years of scientific and teaching experience to his position. His educational background includes a Candidate of Sociological Sciences degree from Moscow State Technological University 'Stankin' (2009) and a Mathematics specialty from Lomonosov Moscow State University (2006), where he also completed additional qualifications as a Specialist in theoretical computer science (2005), Higher education teacher (2011), and Management in Education (2017) from Moscow State Pedagogical University. Samonenko's research spans automata theory , formal languages , information technology in education , and social graph analysis . His scholarly work demonstrates a clear trajectory from theoretical computer science toward applications in educational contexts and organizational structures. He has published extensively on automata theory, face recognition, and the application of social graph analysis to organizational efficiency. His 14 publications over two decades reveal a research evolution from pure theoretical computer science toward more applied work in educational technology and organizational analysis. His recent work focuses on social graph structure in organizations, academic performance analysis using educational data, and the application of formal language theory to practical problems. Letter of gratitude from the Vice-Rector of HSE University (October 2024) HSE University Honorary Diploma (October 2022) Letter of gratitude from the Director of the HSE Lyceum (August 2022) Honorary Diploma from the Higher School of Economics (June 2020) Participant of the Administrative Personnel Reserve program (2019–2020) Best Academic Supervisor in the category 'Student Engagement in Intercampus and Interfaculty Interaction' - 2024 Best Academic Supervisor in the International Student Admissions category – 2024 As Deputy Dean, Samonenko coordinates educational programs, participates in accreditation processes, develops regulatory documents for the educational process, and oversees faculty initiatives. He coordinates interaction between the faculty and both the HSE Lyceum and the general education system. Notably, he was featured in university news as one of the first couples to have a wedding at the Durasov House on Pokrovsky Boulevard, part of the HSE University campus. Samonenko teaches core computer science courses including Algorithms and Data Structures, Python Programming Language, and Programming and Data Analysis in Python across multiple departments at HSE University. His teaching spans the Faculty of Computer Science, Faculty of Economic Sciences, and Faculty of Social Sciences.

• automata theory
• formal languages
• information technology in education
• +3 more