Aleksei Shmelev, Nikita Chervov, and Ivan Gevorkov, researchers from HSE’s International Laboratory of Statistical and Computational Genomics and a recent Data Analysis in Biology and Medicine Master’s graduate, joined forces with two US students to win gold at the 2026 NeuroGolf international machine learning championship, ultimately placing seventh overall. This victory arrives as a new analysis reveals Brazil, Russia, India, China, and South Africa collectively authored 39.2% of publications at ten top-level machine learning conferences between 2020 and 2025. The fifth annual Fall into Machine Learning 2026 conference will be held on October 23-24, 2026, at the HSE Cultural Centre in Moscow, further solidifying Russia’s position within the growing AI community.
HSE Team Achieves Gold at 2026 NeuroGolf Championship
This result follows years of focused development within HSE’s computer science programs and highlights the growing prominence of Russian institutions in the field. The NeuroGolf championship presents a unique challenge, requiring participants to develop algorithms capable of controlling a simulated golf swing, demanding both precision and adaptability in machine learning models. The HSE Scientometrics Centre determined these BRICS nations collectively authored 39.2% of papers presented at ten top-level A* machine learning and artificial intelligence conferences, totaling over 104,000 publications.
Despite this significant contribution, the researchers noted the absence of a fully integrated research network among the BRICS countries, suggesting potential for even greater impact through increased collaboration. The event is a central meeting point for Russia’s AI researchers, developers, and those shaping the future of technology, solidifying HSE’s position as a key hub for innovation.
This conference builds on the momentum generated by recent successes, including the work presented at ICML 2026 in Seoul, South Korea, where several projects from the HSE Faculty of Computer Science received the prestigious Spotlight distinction. The team’s achievement at NeuroGolf also reflects a broader trend of interdisciplinary research at HSE, combining expertise in statistical genomics, data analysis, and computer science, BRICS says.
Ivan Gevorkov, a recent graduate of HSE’s Master’s program in Data Analysis in Biology and Medicine in 2025, brought a unique perspective to the challenge, applying skills honed in analyzing complex biological datasets to the intricacies of algorithmic golf. This exchange of ideas is increasingly common, as researchers recognize the potential for applying techniques developed in one field to address problems in another.
Saraa Ali, a Junior Research Fellow at HSE’s Laboratory of Methods for Big Data Analysis, exemplifies this approach, consistently considering the potential societal benefits of her work. Beyond the competition itself, the NeuroGolf championship is a valuable testing ground for new machine learning algorithms and techniques. The simulated environment allows researchers to rapidly iterate on their designs, evaluating performance in a controlled setting before deploying them in real-world applications, according to BRICS.
This iterative process helps refine models and ensure their robustness and reliability. Researchers at HSE are also exploring methods for improving the efficiency of machine learning algorithms, addressing concerns about resource consumption and computational cost. The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026. Developing more efficient algorithms is particularly important as machine learning models become increasingly complex and data-intensive.
Researchers are also investigating novel approaches to filter design, with a team at HSE MIEM collaborating with the Moscow Technical University of Communications and Informatics to implement a generative synthesis method using machine learning tools. This approach reportedly reduces the filter development cycle from days to minutes, potentially accelerating innovation in microwave electronics and other fields.
Dmitry Vetrov, a Research Professor at the HSE Faculty of Computer Science, was recently recognized with a Yandex ML Prize for his contributions to the development of artificial intelligence in Russia, highlighting the growing national emphasis on this critical technology. The increasing focus on urban studies also benefits from these advancements. Participants in the Spatial Analysis and Modelling of Urban Processes research group at HSE are utilizing open data and machine learning to identify patterns in city layouts, attempting to predict phenomena such as café openings and traffic congestion.
Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies. This work underscores the potential for machine learning to address real-world challenges and improve the quality of life in urban environments.
Aleksei Shmelev, whose research focuses on genomics and the history of human populations, embodies this spirit of applying machine learning to address fundamental questions. The team’s success at NeuroGolf, coupled with the broader research initiatives at HSE, demonstrates a commitment to pushing the boundaries of machine learning and harnessing its power for positive impact.
