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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.
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‘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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Global Integration of Artificial Intelligence in Higher Education Sector: A Bibliometric Analysis

P. 270–276.
Olga Gordienko, Bagrationi K.

In modern perspective, the phenomenon of artificial intelligence (AI) has witnessed considerable advancements, extending its influence across various sectors, with higher education emerging as one of the most significant areas of impact. Researchers who have found the revolutionary potential and essential contributions of AI in boosting educational practices and approaches have extensively emphasized this trend. (George & Wooden, 2023; George & Paul, 2020). The higher education domain is progressively acknowledging AI as a key factor for competitive edge (Hannan & Liu, 2021). This paper aims to examine the existing research on AI implications in the higher education segment. Grounded on the theory of technology diffusion (Rogers, 2003), our research systematically examines the scientific field, identifying emerging research topics. Using SCOPUS as a database for peer-reviewed article selection (publication period 2011–2024) and bibliometric analysis, we provide an insight into the varied geographic spread of research within the realm of AI in education. Our findings shed light on the evolution of models for the adoption of AI technologies in higher education, revealing six main areas of research in the field: (1) teaching and involving students in the educational process using generative AI, (2) using chatbots to improve the educational process, (3) improving the literacy of teachers and students in the field of AI, (4) AI and blockchain in educational practices (5) development of regulations for the use of AI and (6) improving operational processes at universities. This article explains current influences on research prospects in the context of higher education. By offering an understanding of the challenges and opportunities presented by AI, this paper encourages educators to investigate possible uses of AI in the context of higher education.

Language: English
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Keywords: leadershiporganizational learningresistance to changedigitalizationdigital technologiesstrategic learningapplied problems of artificial intelligencecontinuous learningleadership in higher education

In book

Proceedings of the 25th European Conference on Knowledge Management
Vol. 25. Issue 1. , Academic Conferences International Limited, 2024.
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