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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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Как устроен прием в аспирантуру в российских вузах?

Университетское управление: практика и анализ. 2022. Т. 26. № 2. С. 92–104.
Zhuchkova S.

The paper examines the rules and procedures of the doctoral students admission at Russian universities. The inefficiency of the selection process often becomes the subject of discussion in the studies devoted to the low performance of Russian doctoral education, but these studies lack the structured information about the current rules and procedures. The present research closes this gap. Using the quantitative content analysis of the local admission rules of 150 random Russian universities, we estimated the prevalence of various admission practices. Two groups of universities are compared in the study: those with and those without special statuses (federal, national research, pillar, participants of the “5-100” program), since the presence of such a status implies that the university implements a set of measures aimed at the development of doctoral programs. The results of the study show that despite the absence of the regulatory restrictions on the choice of selection tools, Russian universities tend to choose fairly formal procedures: traditional exams and consideration of the formal individual achievements (papers, patents, diploma with honors). More flexible tools that assess the applicant's motivation, his or her research experience, and plans for a dissertation are extremely rare and appear in universities with special statuses. The results of the research can be useful to the heads of the doctoral education departments at Russian universities, as well as faculty involved in the process of the doctoral students admission.

Research target: Education
Language: Russian
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Keywords: аспирантуравступительные испытанияadmissiondoctoral educationleading universitiesэффективность аспирантурыefficiency of doctoral educationentrance examinationsindividual achievementsselection of applicantsведущие вузыиндивидуальные достиженияприем в аспирантуруотбор в аспирантуру
Publication based on the results of:
Educational environment and student experience in higher education (2022)
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