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News
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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Распутывая клубок бюрократии российских университетов: административные профили

Высшее образование в России. 2024. Т. 33. № 11. С. 56–72.
Akuneeva T., Platonova D.

Drawing on the analysis of staff restructuring in universities around the world, the article stresses the growing share of non-academic staff, in particular highly qualified middle and senior administrative staff. The purpose of the article is to analyze how a combination of different categories of administrative staff is represented in universities according to their functions. Based on the cluster analysis of the Monitoring of education markets and organizations (N=92), three groups of universities were identified according to administrative profiles: enterprising, educational and infrastructural. The first cluster is characterized by a high proportion of administrative staff engaged in general management, research, financial issues and information and communication technologies. The second cluster has a high proportion of staff engaged in providing educational activities. The third cluster is characterized by a high proportion of personnel engaged in the maintenance and operation of infrastructure and buildings. Universities with an infrastructural administrative profile are characterized by the presence of a larger area of educational and laboratory facilities per student, however, universities in the sample do not differ in terms of scientific, educational and financial activities.

Research target: Education
Language: Russian
DOI
Text on another site
Keywords: кластерный анализадминистративный ландшафтнеакадемический персоналадминистративный персоналреструктуризация персонала
Publication based on the results of:
Production function of Russian higher education institutions: analysis of features, structure and changes (2024)
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