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News
July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
July 20, 2026
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A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.

 

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О применении методов машинного обучения для оценки рисков лизинговой компании

Социальные и экономические системы. 2024. № 3(53). С. 152–169.
Чудинова О. С., Ramenskaya A.

 The article examines the practical application of machine learning methods to address the issue of assessing the lessee solvency. The first stage comprises BPMN 2.0 business process modelling for leasing transactions. The second stage envisages the construction of two interpretable models (logistic regression and classification tree) to assess the lessee solvency by means of Python libraries. The models were built taking into account the amount and duration of the leasing agreement, the type of equipment, the legal form of the lessee economic activity and the existence of other contracts. The paper compares the quality of the models and interprets the results. The classification tree model outperforms in terms of quality criteria, whereas logistic regression allows quantifying the impact of the lessee characteristics and predicting the probability of lessee arrears. Based on the findings, the article provides recommendations for the practical application of machine learning models to assess the risk of overdue lease payments. 

Research target: Economics and Management Mathematics
Language: Russian
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Keywords: лизингмашинное обучениеоценка рискалогистическая регрессиядерево решений
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