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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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Региональные различия в доступе российских учащихся к дистанционному обучению в 2016– 2022 гг.: эффекты периода COVID-19

Экономическая социология. 2024. Т. 25. № 1. С. 96–116.
Getman A., Adamovich K.

Distance learning is one of the most discussed educational technologies in the world. While some researchers suggest that distance education contributes to reducing socio-economic inequality, other works show its consequences in the form of increased segregation. However, during the COVID-19 pandemic, the transition to this form of education turned out to be an alternative to traditional education, despite all the disputes about its effectiveness and consequences.

The aim of this study is to analyze the regional differences in students’ access to distance learning in 2016-2022, carried out in the paradigm of N. Hillman’s Geography of Opportunities. Using the quasi-experimental method of interrupted time series, the dynamics of differences in the coverage of students with distance learning in 2016-2022 was estimated on the basis of regional statistical reporting forms. Then, using the Pearson correlation method, the relationship of students’ access to distance learning with socio-economic and demographic characteristics of the regions was analyzed. Further, the fixed effects of the pandemic period on students’ access to distance learning in schools in different regions were evaluated.

The results obtained allow us to transfer the concept of the geography of opportunities into the Russian context and confirm N. Hillman’s thesis about the existence of educational «deserts», where the educational opportunities of students are largely explained by a less prosperous context. The COVID-19 pandemic has served as a catalyst for the growth of existing differences. So, at the time of the lockdown in 2020, the readiness of schools to switch to distance learning varied greatly depending on the subject of the Russian Federation. In the most advantageous position were the regions-educational «oases», where during the pandemic the growth of students’ access to distance learning was maximal. At the same time, some growth of this indicator in the regions educational «deserts» turned out to be insufficient to reduce inequality. Thus, the application of the concept of the geography of opportunities in the Russian context highlights the prospects of a decentralized system to reduce social inequality.

Research target: Education
Language: Russian
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DOI
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Keywords: дистанционное обучениеregional differencesцифровое неравенстворегиональные различия Distance LearningCOVID-19social and digital inequalityгеография возможностейCOVID-19Geography of opportunityeducational oasessocio-economic segregation«образовательные оазисы»социально-экономическая сегрегация
Publication based on the results of:
The relationship between self-regulated learning skills, motivation and engagement in the context of learning on online platforms (2024)
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