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News
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Прогнозирование распространения COVID-19 в ЕС с использованием рандомизированного машинного обучения динамических моделей

Информационные технологии и вычислительные системы. 2022. № 3. С. 67–78.
Popkov A., Dubnov Y. A., Popkov Y.

The work is devoted to application of the theory of Randomized Machine Learning to forecasting of the COVID-19 pandemic based on SIR epidemiological model. We propose two modelling variants, the first is based on estimation of SIR model using real case data, the second is based on the idea of modelling transmission coefficient and its prediction. Comparative study of proposed approach is based on a comparison with the standard least squares approach and is carried out on a dataset of several countries of the European Union. It is shown the performance of the proposed approach and its effectiveness and adequacy under conditions of small amount of data with a high level of uncertainty.

Research target: Mathematics Medical Technologies
Language: Russian
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Keywords: моделирование эпидемийSIRSIRrandomized machine learningSARS-CoV-2SARS-CoV-2COVID-19Entropy estimationEpidemic modellingCOVID-19рандомизированное машинное обучениеэнтропийное оценивание
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