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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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Two-dimensional high-throughput on-cell screening of immunoglobulins against broad antigen repertoires

Communications Biology. 2024. Vol. 7. Article 842.
Lomakin Y., Ovchinnikova L., Terekhov S., Dzhelad S., Yaroshevich I., Mamedov I., Smirnova A., Grigoreva T., Igor E. Eliseev, Filimonova I., Mokrushina Y., Abrikosova V., Rubtsova M., Kostin N., Simonova M., Bobik T., Aleshenko N., Alekhin A., Boitsov V., Zhang H., Smirnov I., Rubtsov Y., Alexander G. Gabibov

Identifying high-affinity antibodies in human serum is challenging due to extremely low number of circulating B cells specific to the desired antigens. Delays caused by a lack of information on the immunogenic proteins of viral origin hamper the development of therapeutic antibodies. We propose an efficient approach allowing for enrichment of high-affinity antibodies against pathogen proteins with simultaneous epitope mapping, even in the absence of structural information about the pathogenic immunogens. To screen therapeutic antibodies from blood of recovered donors, only pathogen transcriptome is required to design an antigen polypeptide library, representing pathogen proteins, exposed on the bacteriophage surface. We developed a two-dimensional screening approach enriching lentiviral immunoglobulin libraries from the convalescent or vaccinated donors against bacteriophage library expressing the overlapping set of polypeptides covering the spike protein of SARS-CoV-2. This platform is suitable for pathogen-specific immunoglobulin enrichment and allows high-throughput selection of therapeutic human antibodies.

Research target: Biology Basic Medicine
Language: English
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Keywords: BacteriophagesMonoclonal antibodiesSARS-CoV-2ImmunoglobulinsHigh-throughput screening
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