Final of International Yandex–HSE Olympiad in AI and Data Analysis Held at HSE University

Yandex Education and the HSE Faculty of Computer Science have announced the results of the international AIDAO (Artificial Intelligence and Data Analysis Olympiad) competition. Students from 14 countries took part. For the second year in a row, first place went to the team AI Capybara, which developed the most accurate AI model for an autonomous vehicle vision system.
This year, the olympiad brought together 248 teams from Russia, Kazakhstan, South Korea, Germany, Iran, and other countries. The prize fund amounted to 2,650,000 rubles, shared between the five best teams. Participants tackled applied AI tasks based on real datasets and industry needs.
The assignment for the online qualifying round was created by HSE’s Laboratory of Methods for Big Data Analysis (LAMBDA) and by QRate, a developer of quantum-technology-based secure communication systems. Competitors worked with an algorithm that helps correct errors in the transmission of secret quantum keys, making them more reliable and secure for users. Such technologies are needed to ensure the safe exchange of sensitive information in many fields, including finance, public services and scientific research.
Thirty of the strongest teams advanced to the on-site final, which took place in Moscow. Under the olympiad’s rules, the previous year’s winners—AI Capybara from ITMO University—also took part in the final round.
Ivan Arzhantsev, Dean of the HSE Faculty of Computer Science
‘The seventh AIDAO final vividly confirms that today artificial intelligence and data analysis bring together the strongest students and postgraduates from across the country—and beyond. I am especially pleased that the top teams represent a wide range of universities.
I congratulate all the prize-winning teams. Your work is not simply about solving complex problems, but about a dialogue between disciplines, where mathematics, programming, and scientific curiosity go hand in hand. I would like to thank all participants, mentors and, of course, our partners from Yandex Education—without such cooperation, initiatives like this would not be possible.’
The task for the final stage was developed by Yandex’s autonomous transport team and focused on AI in robotics. Participants had to train an AI model to construct maps of stationary obstacles based on images. Such maps are used for autonomous vehicle navigation in cities and on motorways.
The participants’ solutions were first checked by the Yandex Contest testing system (also used in the All-Russian School Olympiad in Informatics and other competitions) and then evaluated by experts from HSE and Yandex. The most accurate model was created by the AI Capybara team, consisting of Timur Ionov and Daria Ledneva. They became AIDAO champions for the second consecutive year and received one million rubles. Alongside ITMO, the top five also included teams featuring students from MIPT, Skoltech, Moscow State University, the Financial University, Far Eastern Federal University and Saint Petersburg State University of Aerospace Instrumentation (SUAI).
See also:
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).
New Neural Network for Science and Innovation Being Developed at HSE University
HSE researchers are training large language models (LLMs) to understand Russian-language scientific terminology while improving their energy efficiency. The adapted model runs 2.7 times faster and requires 73% less memory than the original open model, allowing it to operate on more affordable hardware. The programme has passed state registration.
HSE FCS Researchers Showcase AI and Bioinformatics Breakthroughs at ICLR 2026
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science, along with students from the AI360: Artificial Intelligence Engineering track of the Applied Mathematics and Information Science bachelor’s programme, took part in ICLR, one of the world’s most prestigious international conferences on machine learning and representation learning. This year’s event was held in Rio de Janeiro, Brazil.
The Future of Cardiogenetics Lies in Artificial Intelligence
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a program capable of analysing regions of the human genome that were previously inaccessible for accurate interpretation in genetic testing. The program adapts large generative AI (GenAI) models for cardiogenetics to predict how specific mutations affect the function of individual genes.
HSE and Yandex Propose Method to Speed Up Neural Networks for Image Generation
A team of scientists at HSE FCS and Yandex Research has proposed a method that reduces computational costs and accelerates text-to-image generation in diffusion models without compromising quality. These models currently set the standard for text-to-image generation, but their use is limited by high computational loads, the company said in a statement.
A Trap for the Advanced Student: How to Break the Habit of Blindly Trusting Neural Networks
Andrei Ternikov, Associate Professor at the St Petersburg School of Economics and Management at HSE University–St Petersburg, has developed a method for conducting online exams that significantly limits students’ ability to use ChatGPT and other AI models to obtain correct answers. Andrei Ternikov spoke to the HSE News Service about his approach—which won the HSE University Autumn Educational Innovation Competition, received an Alfa Future grant, and was presented at an international conference in Japan.
HSE Researchers Train Neural Network to Predict Protein–Protein Interactions More Accurately
Scientists at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a model capable of predicting protein–protein interactions with 95% accuracy. GSMFormer-PPI integrates three types of protein data (including information about protein surface properties) to analyse relationships between proteins, rather than simply combining datasets as in previous models. The solution could accelerate the discovery of disease molecular mechanisms, biomarkers, and potential therapeutic targets. The paper has been published in Scientific Reports.
‘A Trademark of Russia as an Educational Power:’ Marking a Decade of the Open Doors Olympiad
An instructional seminar of the Open Doors International Olympiad has been held at HSE University. Approximately 100 representatives of 24 Russian universities involved in organising the olympiad took part in the event. The seminar focused on updating the participants’ profile maps, organising the work of methodological teams in a digital environment, and ensuring the quality of the methodological content for the olympiad.


