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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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Multisector Bounded-Neighborhood Model: Agent Segregation and Optimization of Environment’s Characteristics

Mathematical Models and Computer Simulations. 2022. Vol. 14. No. 3. P. 503–515.
Akopov A. S., Beklaryan L. A., Beklaryan A.

An approach is presented to study the effects of segregation using the developed multisector bounded-neighborhood model. A model of the evolutionary dynamics of a community consisting of local (indigenous) and external (migrants) populations interacting in an artificial socioeconomic system is proposed, in which the key sectors of the economy are identified: the extraction of raw materials (primary sector, attracting mainly migrants), the manufacturing sector (secondary sector attracting predominantly indigenous people), and low-tech and high-tech services (tertiary and quaternary sectors of the economy attracting migrants and indigenous people, respectively). The formation of jobs in these sectors of the economy is carried out centrally using the previously proposed fuzzy clustering algorithm. Simulation experiments are carried out, and the effects of segregation due to the desire of agents to search for the most desirable jobs in a bounded neighborhood under various scenario conditions are studied. Using the proposed genetic algorithm, an important optimization problem is solved to maximize GDP growth rates and minimize the level of the population’s segregation.

Research target: Mathematics Computer Science
Language: English
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Keywords: genetic algorithmsegregation effectsagent clusteringbounded-neighborhood modelstolerant threshold behavior modelsagent-based modeling of migration and socioeconomic processes
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