جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Natural Language Processing
• Deep Learning
• +۲ مورد دیگر

Elizaveta Vsevolodovna Zhemchuzhina serves as a Researcher and project manager at the National Research University Higher School of Economics (HSE University), affiliated with the Faculty of Computer Science and the Institute of Artificial Intelligence and Digital Sciences. She works within the Laboratory of Theoretical Foundations of Artificial Intelligence Models where she manages applied research projects under supervisor Puchkin N. A. She earned her Bachelor's degree in Applied Mathematics and Computer Science from HSE University in 2023 and has been engaged in research activities since 2021. Her professional trajectory shows progression from research projects at LEA, HSE St. Petersburg (2021-2023) to managing applied projects at HSE University since 2024. Dr. Zhemchuzhina's research focuses on machine learning and data analysis with specialization in natural language processing using deep neural networks. She maintains strong interest in commercialization of scientific results, bridging theoretical AI advancements with practical applications. Her work on the project "Complexity and learnability of artificial intelligence models" reflects her commitment to both theoretical foundations and real-world implementation of AI systems. Her publication record includes research presented at the prestigious AAAI Conference on Artificial Intelligence (2023), demonstrating her contributions to computational linguistics and semantic modeling within the AI field. Professional Role: Project manager for applied research at Institute of AI and Digital Sciences Collaboration: Organizes industry partnerships and integrates HSE students into laboratory work Location: Office T926 at Pokrovsky Boulevard, Building 11 (10:00-18:00)

• matrix analysis
• artificial intelligence
• tensor methods
• +۱ مورد دیگر

Nikolay Evgenievich Yudin serves as a Research Intern at the Research and Educational Laboratory of Matrix and Tensor Methods in Machine Learning and as a Teacher 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). He began his position at HSE in 2023 and holds proficiency in English. Research Intern: Faculty of Computer Science / Institute of Artificial Intelligence and Digital Sciences / Research and Educational Laboratory of Matrix and Tensor Methods in Machine Learning Teacher: Faculty of Computer Science / Department of Big Data and Information Retrieval Yudin earned his Bachelor's degree in Applied Mathematics and Computer Science from HSE University in 2024. His academic journey reflects a strong foundation in mathematical approaches to computer science problems. Yudin's research focuses on the intersection of mathematical methods and artificial intelligence, with particular emphasis on matrix analysis, tensor methods, and deep learning applications. His work bridges theoretical mathematics with practical AI implementations, contributing to more efficient neural network architectures and processing techniques. He actively participates in the academic community through teaching and research collaborations. His notable publication 'Group and Shuffle: Efficient Structured Orthogonal Parametrization' presented at NeurIPS 2024 demonstrates his contribution to optimizing neural network fine-tuning processes. The research introduces innovative methods for data processing that reduce memory requirements while maintaining performance in image generation, analysis, and text model training. Bonus for publication in a journal from List A (and equivalent scientific publication) (2025–2026) High Professional Potential Group (HSE Personnel Reserve) Category: New Researchers (2025) Yudin teaches 'Fundamentals of Matrix Computations' across multiple programs including Bachelor's and Master's degrees in Applied Mathematics and Computer Science. His teaching spans both the Faculty of Computer Science and Faculty of Economic Sciences, demonstrating the interdisciplinary nature of his expertise. He previously completed internships at Yandex as a Development Intern (2022) and Trainee Machine Learning Developer (2023). As a member of the Research and Educational Laboratory of Matrix and Tensor Methods in Machine Learning under the supervision of Rakhuba M.V., Yudin contributes to cutting-edge research in mathematical approaches to AI. He participated in the workshop 'The Mathematics of Machine Learning' which focused on mathematical aspects underlying modern machine learning directions.

• Molecular evolution
• Genomic epidemiology
• Virology
• +۳ مورد دیگر

Galina Viktorovna Klink is an Associate Professor at the Faculty of Computer Science and Senior Research Fellow at the International Laboratory of Statistical and Computational Genomics within the Institute of Artificial Intelligence and Digital Sciences at the National Research University Higher School of Economics (HSE). She joined HSE in 2024, bringing with her 9 years of scientific and teaching experience in computational biology and genomics. She holds a Candidate of Biological Sciences degree (2021) from the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences and a Biochemistry specialty (2012) from Moscow State University named after M.V. Lomonosov. Her academic journey includes postgraduate studies at Lomonosov Moscow State University's Faculty of Bioengineering and Bioinformatics (2013-2016) and research work at the Institute for Information Transmission Problems, Russian Academy of Sciences (2015-2023). Dr. Klink's research focuses on molecular evolution, genomic epidemiology, virology, and adaptive landscape analysis. Her work bridges computational methods with biological applications, particularly in viral evolution and genomic analysis. She has made significant contributions to understanding HIV-1 evolution, SARS-CoV-2 transmission patterns, and adaptation mechanisms in extreme environments. Her publication record shows a strong trend in applying computational genomics to solve biological problems, with recent work spanning viral evolution (HIV and SARS-CoV-2), environmental adaptation in organisms, and ecological monitoring. Her research demonstrates interdisciplinary expertise connecting computer science, biology, and environmental science. High Professional Potential Group (HSE Personnel Reserve) Category: New Researchers (2025) Dr. Klink teaches Introduction to Computational Genomics and the Research Seminar 'Data Analysis in Life Sciences' for third-year bachelor's students in Applied Mathematics and Computer Science. She is actively involved in research projects at the International Laboratory of Statistical and Computational Genomics, where she recently participated in outreach events including lectures at Zaryadye Park. Her laboratory work focuses on statistical and computational approaches to genomic analysis, with applications in virology and evolutionary biology.