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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.
June 4, 2026
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.

 

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Optimal Design of Copper-Doped ZnO Heterostructures for Photocatalytic and Photoeletrochemical Performance: A Combined Experimental and DFT Study

The Journal of Physical Chemistry Letters. 2026. Vol. 17. No. 17. P. 5099–5107.
Wary R. R., Mahato B., Sharma N., Choudhury S. P., Kazakova E., Ostovari Moghaddam A., Prezhdo O., Vasenko A., Das A.

Development of heterojunction materials is an efficient approach for enhancing photocatalytic and photoelectrochemical processes. We develop a ZnO/CuO heterojunction via Cu doping into a ZnO matrix. Structural characterization confirmed that Cu was effectively incorporated into ZnO without any structural changes. The improved optical properties and reduced charge carrier recombination of the doped samples were confirmed by PL, EIS, and TRPL analyses. DFT was used to characterize the modified electronic properties of the materials. The optimal sample (ZnO with 3 wt % Cu) exhibited a photocurrent density of 2.05 mA/cm2 at the onset potential of 0.60 V vs Ag/AgCl (1.21 V vs RHE), which is four times superior to that of bare ZnO. As an application, a complete photocatalytic degradation of methylene blue dye was observed by the optimal sample, attributed to synergy between ZnO and CuO, induced by appropriate Cu-doping.

Research target: Chemistry Materials Technologies
Language: English
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Keywords: Photocatalysis dopingDensity functional theory (DFT)heterojunction materials
Publication based on the results of:
Quantum devices and technologies of the new generation (2025)
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