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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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Loan Portfolio Dataset From MakerDAO Blockchain Project

IEEE Access. 2024. Vol. 12. P. 24843–24854.
Chaleenutthawut Y., Davydov V., Evdokimov M., Kasemsuk S., Kruglik S., Melnikov G., Yanovich Y.

Decentralized finance (DeFi) offers a range of financial instruments and services that leverage the capabilities of web3 technology. Maker protocol, which enables users to obtain loans backed by cryptocurrencies, is one of them. Unlike traditional banks, Maker’s data is transparently recorded on the Ethereum blockchain. In this research paper, we focus on analyzing the lending aspect of Maker from a traditional finance perspective. To achieve this, we create a unique dataset with loan portfolios from the MakerDAO project, making it the first dataset of its kind in the DeFi field. This publicly available dataset contains essential financial characteristics related to borrowing, including balance, loss given default, annual equivalent rate, and probability of default. Additionally, we develop a specialized mathematical model tailored specifically to this project. This model allows us to estimate the probability of default by considering the presence of crypto-collateral and utilizing Brownian motion passage levels. The results of this study provide valuable insights into lending practices in DeFi projects. They also help bridge the gap between traditional finance and blockchain-based financial services.

Research target: Computer Science
Language: English
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Keywords: управление рискамифинансыrisk managementfinanceknowledge discoveryмодели данныхBrownian motionБроуновское движениеstandardsблокчейнData modelsдецентрализованные приложения blockchainportfoliossmart contractsDecentralized applications
Publication based on the results of:
Research and development of data transmission and processing methods for the organization of energy efficient and secure data transmission in the Internet of Things networks (2024)
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