Find Academic Supervisors

• 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.

Rodriguez Zalepinos Ramon AntonioView profile

Associate Professor

• distributed systems
• databases
• Big data
• +6 more

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.

• Virtual Reality
• Scientific Visualization
• Computer Graphics
• +4 more

Aleksandr Zagarskikh is an Associate Professor at the Game Development School of ITMO University, specializing in virtual reality, scientific visualization, and high-performance computing. He has led projects in quantum chemistry visualization, flight simulators, and urban simulation technologies. Developed real-time graphics systems for ultra-realistic image synthesis Created high-performance network protocols for distributed visualization Current research focuses on big data decision-making in finance and multiscale urban modeling His work spans predictive modeling, GPU optimization, and cloud-based infrastructure visualization, with publications in Procedia Computer Science. He teaches courses in game technologies, VR, and scientific computer graphics.

• convex optimization
• optimal transport
• distributed optimization
• +1 more

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.

• Macroeconomic models
• Forecasting macroeconomic indicators
• Models of foreign economic activity
• +3 more

Aleksey Aleksandrovich Uzhegov is a Senior Lecturer at the Department of Applied Economics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE), where he has been employed since 2014. He also serves as a Research Fellow at the Scientific and Educational Laboratory of Macrostructural Modeling of the Russian Economy. His academic journey at HSE began with a Bachelor's degree in Economics (2015), followed by a Master's degree in both general Economics and Applied Economics (2017). Uzhegov's research focuses on macroeconomic modeling, particularly in consumer behavior, forecasting macroeconomic indicators, and foreign economic activity models. His work demonstrates a consistent emphasis on developing sophisticated mathematical approaches to economic modeling, including the innovative application of Relaxation of Complementary Slackness Conditions in general equilibrium frameworks. His research trajectory shows a progression from theoretical foundations toward practical applications in Russian economic contexts. His publications reveal a strong concentration on Russian economic modeling across multiple dimensions - from consumer behavior and household economics to production sectors and foreign economic activity. Uzhegov frequently collaborates with established researchers like Pilnik N.P. and Stankevich I.P., indicating integration within HSE's research ecosystem. His work appears in both Russian and international publications, demonstrating cross-cultural academic engagement. Gratitude from the Faculty of Economic Sciences of HSE (December 2024) Gratitude from the Department of Applied Economics (February 2020) Best Teacher - 2025 High Professional Potential Group (HSE Personnel Reserve) Category 'New Teachers Under 30' (2019–2020) Uzhegov actively supervises student research, with multiple bachelor's theses completed under his guidance focusing on macroeconomic modeling, inflation transmission channels, and international trade. He leads a student team in phygital racing and founded the Armamentario racing community, demonstrating commitment to student development beyond academic instruction. His teaching portfolio includes advanced courses in time series analysis, panel data analysis, and dynamic optimization in economics and finance, reflecting his methodological expertise.

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

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

• human capital
• educational policy
• educational standards
• +4 more

Irina Abankina is a Tenured Professor (2011) and Distinguished Professor (2019) at the National Research University Higher School of Economics (HSE University), where she has been employed since 2001. She holds dual appointments as Professor at the Institute of Education's Department of Educational Programmes and as Senior Research Fellow at the Institute of Education's Centre for Financial and Economic Decisions in Education. Her extensive career spans over two decades at HSE, where she has made significant contributions to educational economics and policy. Abankina earned her Candidate of Sciences (PhD equivalent) in Mathematical and Instrumental Methods in Economics in 1985 and holds a degree in Economic Cybernetics from Lomonosov Moscow State University (1979). Her educational background in mathematical economics forms the foundation of her analytical approach to educational policy and finance. Professor Abankina's research focuses on critical issues in educational economics and policy, including human capital development, educational standards implementation, financing mechanisms for various education levels, and systemic reforms in education. Her work particularly emphasizes preschool and higher education financing, with numerous publications examining international comparisons, budget allocation strategies, and policy effectiveness. She has pioneered research on personalized funding models, teacher compensation structures, and the economic aspects of educational quality assurance. Her scholarly output demonstrates consistent focus on the economic dimensions of educational policy, with particular attention to how financial mechanisms impact educational accessibility, quality, and equity. Analysis of her recent publications reveals growing interest in regional educational disparities, university-city relationships, and innovative funding approaches that respond to changing demographic and economic conditions. Best Teacher Award (2023) Best Teacher Award (2021) Best Teacher Award (2015) Professor Abankina actively supervises doctoral candidates, having guided O. Leshukov's 2020 dissertation on regional higher education networks and A. Melikyan's 2019 research on university export activities. Her mentorship extends to numerous master's students through courses in Economics, Public Economics, and Foundations of Higher Education. She serves on multiple editorial boards including Экономика образования (2011), Справочник руководителя дошкольного учреждения (2005), and Вопросы образования (2003), demonstrating her commitment to advancing educational scholarship. As a leading researcher at HSE's Institute of Education, Abankina contributes to several major research initiatives examining educational finance, policy implementation, and institutional development. Her work with the Centre for Financial and Economic Decisions in Education focuses on practical applications of economic theory to educational management challenges, particularly in resource allocation and budget optimization across different educational sectors.

• Poverty
• Social Stratification
• Social Policy
• +3 more

Ekaterina Dmitrievna Slobodenyuk serves as an Associate Professor at the Department of Applied Economics, Faculty of Economic Sciences, and as a Senior Research Fellow at the Center for Stratification Studies, Institute of Social Policy, at the National Research University Higher School of Economics (HSE). She has been with HSE since 2012, accumulating 14 years of scientific and teaching experience. Dr. Slobodenyuk earned her Candidate of Sociological Sciences degree in 2014 from HSE with a dissertation on 'The Poor in Modern Russia: Group Structure and Social Dynamics.' Her educational background includes a Master's degree (2011) and Bachelor's degree (2009) in Sociology from HSE. She has also completed specialized training through the World Bank, Essex Summer School in Social Science Data Analysis, and various workshops on social policy and research methodologies. Her research focuses on poverty, social stratification, the lower class, and social policy , with particular attention to income inequality, social mobility, and human capital development in Russian society. Dr. Slobodenyuk has made significant contributions to understanding the dynamics of poverty among Russian professionals, the relationship between subjective and objective poverty measures, and the impact of economic crises on social stratification. Her work employs both quantitative and qualitative methodologies to analyze complex social phenomena. The analysis of her most recent publications reveals a consistent focus on social stratification in Russia, with particular emphasis on professional poverty, wealth inequality, and social mobility patterns. Her research demonstrates how economic instability affects different social groups and how perceptions of inequality influence human capital investments. Dr. Slobodenyuk's work bridges theoretical sociology with practical policy implications, contributing to evidence-based social policy development in Russia. Among her notable achievements are the 'Recognition - 10 years of successful work' medal from HSE, multiple publication allowances for research in prestigious journals, and recognition as a winner of the competition for the best Russian-language scientific works. She has also been included in HSE's high professional potential group and the 'Future Professors' category, reflecting her significant contributions to academic life. Dr. Slobodenyuk has secured funding for numerous research projects, including grants from the Russian Science Foundation (RSF) and the Russian Foundation for Basic Research (RFBR). Her collaborative work with leading sociologists like Svetlana Mareeva has resulted in influential publications and monographs that have shaped contemporary understanding of Russian social structure. She actively supervises research projects and participates in major academic conferences both in Russia and internationally. She leads and participates in several research teams at HSE, particularly within the Center for Stratification Studies at the Institute of Social Policy. Her work connects with broader research initiatives examining social cohesion, economic inequality, and human capital development in post-Soviet societies. Dr. Slobodenyuk's research contributes to interdisciplinary efforts to understand how social structures evolve during periods of economic and political transformation.

Khritankov Anton SergeevichView profile

Associate Professor

• Machine Learning
• Software Engineering
• MLOps
• +3 more

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.

Ruslan A. BaryshevView profile

Researcher

• Library Science
• Digital Humanities
• Information Systems
• +3 more

Ruslan A. Baryshev, PhD, serves as the Vice-Rector for Science at Siberian Federal University . His research spans library science , digital humanities , and information systems , focusing on smart library development , 3D modeling for cultural heritage , and university knowledge management . Key Research Areas : Proactive library ecosystems, AI in education, digital platform integration for intellectual property, cyberpsychology, and user behavior in digital environments. Recent Publications (2024–2023) address trends in digital transformation of libraries, 3D modeling for lost architecture, and data governance in academic settings. Collaborative Projects include patents on railway engineering systems, virtual heritage reconstructions, and distributed digital ecosystems for scientific data. His work emphasizes user-centric library services and information competence development .

Valery Vladimirovich ZavoruevView profile

Professor

• Atmospheric Pollution Monitoring
• Environmental Science
• Air Quality Assessment
• +9 more

Valery Vladimirovich Zavoruev is a Professor at the Department of Ecology and Nature Management within the School of Ecology and Geography at Siberian Federal University (SibFU) in Krasnoyarsk, Russia. He holds a Doctor of Biological Sciences degree with his dissertation focusing on plankton distribution in aquatic ecosystems. His academic career spans several decades with consistent contributions to environmental science and atmospheric monitoring. Doctor of Biological Sciences (2004) Krasnoyarsk State University (Education) Office: pr. Svobodny, 79, building 4, room 44-13 Phone: +7 (391) 206-21-34 Professor Zavoruev's research interests primarily focus on atmospheric pollution monitoring, ecological design, and nature management. His work extensively examines PM2.5 concentrations, benzo(a)pyrene pollution, and the impact of Siberian forest fires on urban air quality. He has developed computational systems for environmental monitoring and conducted significant research on the Yenisei River's influence on Krasnoyarsk's winter atmospheric conditions. His studies often involve spatial analysis of pollution patterns and the development of monitoring technologies. Recent publication trends show Professor Zavoruev has been particularly active in air quality research (2022-2024), with numerous publications on suspended particulates, seasonal pollution patterns, and the development of monitoring systems. His work demonstrates a consistent focus on practical environmental monitoring solutions with immediate applicability to urban pollution challenges in Siberia. The research spans atmospheric science, environmental engineering, and computational environmental analysis. His scientific contributions include: Development of atmospheric monitoring systems Analysis of PM2.5 distribution patterns Studies on river-atmosphere interactions Research on pollution sources from heating systems Wildfire smoke impact assessments Long-term pollution trend analysis Professor Zavoruev regularly collaborates with researchers including Zavorueva E.N., Sokolova O.V., Yakubailik O.E., and Tokarev A.V. on environmental monitoring projects. He teaches courses in Ecology, Environmental management, Ecological design and expertise, Comprehensive assessment of the natural and production potential of the territory, and Modern problems of ecology and nature management, contributing significantly to the education of future environmental scientists.

• Algorithms
• Information System Architecture
• Petri Nets
• +5 more

Alexey Alexandrovich Mitsyuk serves as Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE), where he also holds the position of Deputy Dean for Research and Senior Research Fellow at the Research and Educational Laboratory of Process-Oriented Information Systems (POIS). He has been working at HSE since 2013 with 12 years of scientific and teaching experience. Mitsyuk is also a member of the Academic Council of HSE. His educational background includes a PhD from HSE (2019) with a dissertation on "Correction of process models while preserving their structure based on event logs" and a specialist degree in Applied Mathematics from Moscow State Institute of Electronics and Mathematics (2009). He is actively involved in professional associations including IEEE Task Force on Process Mining and BPM Association. Mitsyuk's research focuses on process mining, Petri nets, software architecture, data visualization, and algorithms. His work bridges theoretical computer science with practical applications in software engineering and educational analytics. He has supervised numerous student theses across bachelor's and master's programs, with students completing research on topics ranging from software architecture analysis to process mining applications. His publication record shows consistent contributions to the field of process mining and software engineering, with recent work focusing on advanced visualization techniques, process model correction, and applications of process mining in educational and mobile contexts. His research demonstrates a clear progression from theoretical foundations of process modeling to practical applications in software development and education. Honorary certificate from HSE (May 2025) Gratitude from the Faculty of Computer Science (August 2022) Best teacher awards (2015-2025) High professional potential group (HSE reserve staff) Competition for best Russian-language scientific works (2024) Mitsyuk has coordinated research activities of the faculty, created new research groups, monitored grants, and analyzed publication activities. He led a student team that won first place in the BPI Challenge 2017 student category. His teaching includes courses on Software Systems Architecture and Mathematical Methods of Process Analysis in Software Engineering. He is actively involved in numerous conferences as committee member and reviewer, particularly in the fields of process mining and software engineering.

• Bayesian methods
• Machine learning
• Probability theory
• +2 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.

Saprykina Natalia AnatolyevnaView profile

Associate Professor

• Additive Manufacturing
• Selective Laser Melting
• Materials Science
• +3 more

Natalia Anatolyevna Saprykina serves as Associate Professor and Deputy Director at the Industrial Technology Department of Yurga Technological Institute, a branch of Tomsk Polytechnic University. With a Candidate of Technical Sciences degree (equivalent to PhD), she has established herself as a prominent figure in mechanical engineering education and research within the Siberian academic community. Education: Graduated in 2000 from Yurga branch of Tomsk Polytechnic University with specialization in Mechanical Engineering Technology Completed postgraduate studies (2008-2011) at Tomsk Polytechnic University in specialty 05.02.07 - Technologies and equipment for mechanical and physical-technical processing Defended PhD thesis in 2013 on 'Improving the technology for forming the surface layer of products obtained by layer-by-layer laser sintering' Extensive professional development with over 20 advanced training courses from 2014-2025 in areas including neural networks, digital education, and smart manufacturing technologies Dr. Saprykina's research focuses on additive manufacturing technologies, particularly selective laser melting and sintering processes. Her work centers on improving surface quality and accuracy of metal prototypes, with special emphasis on aluminum-silicon-magnesium (AlSiMg) alloys and cobalt-chromium-molybdenum (Co-Cr-Mo) systems. She has published extensively on powder characteristics, process parameter optimization, and microstructure-property relationships in additively manufactured components. Her research bridges fundamental materials science with practical industrial applications. Analysis of her recent publications reveals a strong trajectory in metal additive manufacturing research, with increasing focus on multi-component alloys, process optimization, and educational applications of these technologies. Her work demonstrates consistent progression from fundamental studies of laser-material interactions to applied research on specific alloy systems with industrial relevance. Dr. Saprykina actively mentors students in research projects related to additive manufacturing, robotics, and smart production technologies. Her educational contributions include developing electronic courses and implementing project-based learning approaches in mechanical engineering education. She has been instrumental in modernizing the curriculum at Yurga Technological Institute to incorporate emerging digital manufacturing technologies and pedagogical methods. Her professional activities demonstrate strong integration of research and teaching, with particular emphasis on preparing engineering students for modern manufacturing environments through hands-on experience with advanced production technologies and digital tools.