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.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
Teaching a Machine to Read the Past: HSE Develops Neural Network to Decipher Manuscripts
Diaries and letters are an invaluable resource for humanities scholars. But what can be done when the text is impossible to read? At the HSE Faculty of Humanities, this challenge has been translated into the language of mathematics: a team of philologists, historians, and machine learning specialists has created an information system that not only recognises illegible handwriting but also helps analyse archival content.
Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.
HSE and Nazarbayev University: Scientific and Educational Cooperation
In April 2026, HSE University welcomed an official delegation from Nazarbayev University. The visit primarily focused on establishing cooperation between the two universities, expanding partnership ties, and developing joint projects in support of strengthening bilateral relations between Russia and Kazakhstan.
‘Meet Professors, Gain Experience’: Uzbek Lyceum Students Undertake Placement at HSE
The fourth off-site school organised under the Lyceum Classes project has taken place with the support of HSE University and implemented by the HSE Department of Internationalisation. This year, 79 students from International House Tashkent and Interhouse Lyceum came to HSE. The programme includes an introduction to the university, the opportunity to attend classes, and tours around Moscow.
HSE and Yandex Propose Method to Speed Up Neural Networks for Image Generation
A team of scientists at HSE FCS and Yandex Research has proposed a method that reduces computational costs and accelerates text-to-image generation in diffusion models without compromising quality. These models currently set the standard for text-to-image generation, but their use is limited by high computational loads, the company said in a statement.
Mathematical Physics at HSE University Goes International
The HSE University International Laboratory for Mirror Symmetry and Automorphic Forms and the Beijing Institute of Mathematical Sciences and Applications (BIMSA) held a joint online conference on mathematical physics. The results of the laboratory research presented at the event will be published in leading academic journals.
Transport and Cities: HSE’s Faculty of Urban and Regional Development Co-Organises First International Transport and Logistics Forum
The HSE Faculty of Urban and Regional Development acted as a partner in organising the first International Transport and Logistics Forum, held in St Petersburg in early April and bringing together more than 6,000 participants from 82 countries. With the faculty’s support, a key session was held entitled ‘A Driverless Future: How Driverless Transport is Changing the Concept of Spatial Organisation from the City to the State’ and moderated by Russian Minister of Transport Andrei Nikitin.