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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Университетские объединения в России: четыре волны образовательной политики

Университетское управление: практика и анализ. 2017. Т. 21. № 3. С. 112–120.
Romanenko K., Lisyutkin M.

This article is aimed at identifying key peculiarities of such type of educational policy as university mergers - their main stages, types and declared aims. During the research we analyzed cases of university mergers and takeovers since 1990s to present moment which allowed for identifying and describing four Russia - specific educational policy waves. Based on the authors’ classification, there are given basic characteristics for each of identified waves: «Universitization», «Federal Universities», «Optimization», «Pillar Universities». Classification grounds include such possible forks as similarity of university academic profiles, formal hierarchy of uniting organizations, remote geographical positions, and, importantly, subject of decision making concerning mergers. Besides that, the article for the first time introduces into the context of university merger research such notions as «mobilization», «optimization », and «reprofilization» describing basic motives of university reorganization. Prevailing motive behind each of the merger waves is also given. The research presented can be continued in the form of detailed analysis of university mergers process with particular attention to «human factor» of merger procedure and creation of new organizational culture in merged universities. The results of this research can be used in planning potential mergers and evaluating completed university reorganizations.

Research target: Economics and Management Sociology (including Demography and Anthropology Education
Priority areas: management
Language: Russian
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DOI
Text on another site
Keywords: образовательная политикаhistory of higher educationистория высшего образованияuniversity mergersoptimization and reorganization of the universitiesуниверситетские объединенияоптимизация и реорганизация университетовeducational policy
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