‘You Need to Know a Lot of Ideas and Algorithms, Come Up with Something Unconventional’

A student of the HSE Faculty of Computer Science, Andrey Kuznetsov, has become the winner of the 2024 Data Fusion Contest. He took first place in solving geoanalytics tasks, and also won the special ‘Companion’ category. The competition took place as part of the 2024 Data Fusion conference on big data and AI technologies. Researchers from HSE University presented the results of their work and demonstrated applied developments at the conference.
The 2024 Data Fusion Contest took place as part of the conference, with more than 1550 participants from 33 countries registered, including Russia, Armenia, the UK, Kazakhstan, the Netherlands, and the USA (for a full list, please see the contest’s website in Russian). The tournament involved both advanced IT specialists and those just starting their professional journey.
First place in the ‘Geoanalytics’ category was taken by Andrey Kuznetsov, a second-year student of the ‘Applied Mathematics and Information Science’ Bachelor’s programme at the HSE Faculty of Computer Science. He also won the special ‘Companion’ category for the best open solutions to each of the tasks published by participants before the end of the competition.
Andrey Kuznetsov, second-year student of the Bachelor’s programme ‘Applied Mathematics and Information Science’
‘During the contest, as well as completing the task, you had to review materials on the topic, necessary literature, and existing approaches and methods. In the competition, there were two tasks—'Geoanalytics' and 'Churn Models.' In the first one, information about clients' card transactions was given, and you needed to predict the probability of cash withdrawal at a particular ATM. In the second one, using bank data for six months, you needed to forecast the probability of a decrease in user activity over the next six months.
Machine learning contests usually take a long time. We were given almost two months to complete the tasks. To achieve good results, it is necessary to analyse solutions from past competitions, as well as spending a lot of time on training, practice, and studying new material. You need to know a lot of ideas and algorithms in order to come up with something unconventional.
Compared to previous years and other competitions in general, this time the tasks felt more challenging. The competition took place on a convenient and stable platform, with many experienced specialists participating. That is why the joy of victory is even stronger!’
Scientists from HSE University participated in Data Fusion 2024. Alexey Naumov, Academic Supervisor of the HSE AI Research Centre, presented cutting-edge research directions and some achievements of HSE scientists at the session 'Overview of Key AI Research in Russia.' 'One of our recent achievements was an article that made it into the top 5% at the international conference AISTATS 2024. We demonstrated that training Generative Flow Networks (GFlowNets) is equivalent to a certain regularised reinforcement learning task. This discovery allows the direct application of a large number of existing reinforcement learning techniques and algorithms to improve the performance of generative flow networks,' he explained.
This year, the AI Research Centre has already had 8 publications accepted at top conferences such as ICML, NeurIPS, ICLR, AISTATS, and others. Researchers are also working on diffusion models, which enable the generation of complex protein and molecule structures, as well as data lying on bounded manifolds.
The AI Research Centre also presented applied developments for industrial partners. For example, a specialised NLP model, developed for Sber, generates readable descriptions of reasons for customer inquiries in order to improve the interpretability of themes obtained from clustering the flow of customer inquiries. This solution allows the bank to speed up the processing of over 300,000 customer inquiries per day.
Other representatives of HSE University also took part in Data Fusion 2024. Sergei Kuznetsov, Head of the School of Data Analysis and Artificial Intelligence at the HSE Faculty of Computer Science, moderated the case session 'Does Symbolic AI Have a Future, or Will Neural Networks Win Everything?' discussing the prospects of symbolic AI in light of tasks related to creating trusted artificial intelligence. During this session, Alexey Neznanov, Associate Professor at the HSE School of Data Analysis and Artificial Intelligence, delivered a presentation on 'Knowledge Management and Hybrid Intelligent Systems in the Corporate Environment, 20 Years Later.'
Sergei Kuznetsov also took part in the session 'Progress or Regression: Where is Artificial Intelligence Leading Us—Experts on Trends' and held a discussion with Sankar Kumar Pal, President of the Indian Statistical Institute, on 'India's Experience: Development of AI Technologies and DS-Science.'

Olga Dragoy, Director of the HSE Centre for Language and Brain, participated in the plenary discussion 'The Intelligence of Large Generative Models—Is the Birth of Thought from Language Possible?', where experts discussed the possibility of extracting intelligence from speech, searching for a basis for AI development, and forming an environment in which AI will evolve.
HSE scientists also presented the results of their research at a poster session:
Konstantin Vishnevsky, Director of the Centre for Strategic Analysis and Big Data at the HSE Institute for Statistical Studies and Economics of Knowledge (ISSEK), and Maria Svarchevskaya, a leading expert at ISSEK, gave a presentation on 'The iFORA System for Intelligent Big Data Analysis.'
Alexander Kirdeev, Research Assistant at the International Laboratory of Bioinformatics of the HSE AI and Digital Science Institute, presented on 'Predictive Models in Personalised Medicine.'
Maria Sakirkina, Chief Analyst of the HSE Faculty of Geography and Geoinformation Technology, presented on 'Geoanalytics and Geodata: Evaluation of Natural Climatic Risks.'
The Data Fusion 2024 conference took place in Moscow on April 17th–18th. The event was attended by the Prime Minister of Russia Mikhail Mishustin, Deputy Prime Minister Dmitry Chernyshenko, First Deputy Governor of the Bank of Russia Olga Skorobogatova, President and Chairman of the Board of VTB Bank Andrey Kostin, President of Rostelecom Mikhail Oseyevsky, CEO of VK Vladimir Kiriyenko, and other businessmen and public officials, leading scientists, and experts in big data analysis and AI technologies.
See also:
‘Working with AI Solves a Wide Range of Engineering Problems’
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
A New Section on AI and a Prizewinning Paper: Early-Career HSE Researchers Take Part in IEEE EDM Conference
The 27th IEEE International Conference of Young Professionals in Electron Devices and Materials (EDM) has taken place in the Altai Republic. This year, researchers from HSE University presented the results of their research and were involved in organising a new section on artificial intelligence. A paper by HSE master’s student Rodion Sidorenko was awarded third place in the research paper competition at the conference.
‘AI Enables Researchers to Tackle More Complex and Important Problems’
In late July 2026, Dmitry Rybin, a graduate of the HSE Faculty of Mathematics who is now working in China, used ChatGPT to disprove a longstanding mathematical hypothesis. In an interview with the HSE News Service, he discussed AI's ability to make discoveries in mathematics, reflected on his time at HSE University, and spoke about his doctoral research at the Chinese University of Hong Kong.
AI for Doctors: HSE Faculty of Computer Science Delivers Course for Russian University of Medicine Students
In June 2026, the HSE Faculty of Computer Science (FCS) completed a course on the use of artificial intelligence in medicine for first-year Paediatrics students at the Russian University of Medicine. The course was delivered with support from a grant awarded to HSE University under the Artificial Intelligence federal project, part of the national project ‘Data Economy and the Digital Transformation of the State.’
HSE University to Launch New AI Supercomputer
HSE University is preparing to launch its second supercomputer. The new cluster will be primarily dedicated to artificial intelligence (AI) workloads and will complement the existing cHARISMa supercomputer. It is scheduled to become operational by the end of 2026.
HSE & VK Engineering and Mathematics School Showcases 13 Projects at 10th Demo Day
The 10th Demo Day of the Joint HSE & VK Engineering and Mathematics School was held at the VK Moscow office. Students of the three workshops presented the results of 13 projects in the fields of artificial intelligence, information security, and digital platforms. Students worked on the development of recommendation services, systems for psycholinguistic text analysis and speech processing, methods for identifying celebrities in videos, algorithms for determining the toxicity of memes, security mechanisms for AI systems, and approaches to improving the effectiveness of neural network models.
Tabular Data Anonymisation Solution for Safe Use in AI Systems Developed at HSE University
The AI and Digital Science Institute at the HSE Faculty of Computer Science has developed a tabular data anonymisation service designed to prepare corporate datasets for use in analytics and AI applications. The solution can identify personal data in structured datasets, apply consistent and reproducible anonymisation rules, and generate the artifacts required for quality control, auditing, and subsequent use of data in secure environments.
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.
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
HSE Graduate’s AI Project Wins at TECH & AI Awards
Daria Davydova, graduate of the HSE Graduate School of Business and Head of the AI Implementation Unit at the Artificial Intelligence Department of Alfa-Bank, received a prize at the TECH & AI Awards. She was awarded for the best AI solution for optimising business processes. The winners were determined as part of the VII Russian Summit and Awards on Digital Transformation (CDO/CDTO Summit & Awards).


