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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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Microfluidic–nanophotonic sensor for on-chip analysis of complex refractive index

Applied Physics Letters. 2024. Vol. 124. No. 6. Article 063701.
A. Kuzin, Panda K., Chernyshev V., Florya I., V. Kovalyuk, An P., Golikov A., G. Chulkova, Kolesov D., Gorin D., G. Goltsman

Photonic biosensors based on photonic integrated circuits (PICs) and microfluidic channels (MFCs) have become the subject of intensive research for point-of-care (POC) device applications. In the presented work, we demonstrate the possibility of identifying the complex refractive index (RI) of analyzed liquids through the optimization of the geometry configuration of MFCs under PICs by experimental and numerical approaches. Our results suggest that the real and imaginary parts of the RI for analytes under study can be determined from spectrum of devices with optimized MFCs width. This work paves the way for new opportunities to identify the presence and concentration of biological markers by using RI sensors for in situ POC applications.

Research target: Physics Nanotechnologies
Language: English
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Keywords: Photonic integrated circuits (PICs)Photonic biosensormicrofluidic channels (MFCs)complex refractive indexФотонный биосенсорфотонные интегральные схемы (PIC)микрофлюидные каналыкомплексный показатель преломления
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