Winners of the International Olympiad in Artificial Intelligence Admitted to HSE University
In mid-August, Bulgaria hosted the finals of the first International Olympiad in Artificial Intelligence (IOAI) among high school students. The Russian team demonstrated excellent results, winning gold medals in the scientific round, silver medals in the practical round, and coming first in both rounds overall. This year two members of the Russian team were accepted into the programmes of the HSE Faculty of Computer Science.
In the final round of the competition, the Russian team faced off against school students from 39 countries, including China, Japan, Canada, Sweden, the Netherlands, and the USA. The Central University (CU), established with the support of T-Bank, was responsible for training the team.
Currently, two members of the Russian team, Nikita Kurlaev and Andrey Gritsaev, are first-year students at HSE University. They talked to HSE News Service about the Olympiad, the admission process, and their plans for the future.
Nikita Kurlaev
About the Olympiad
— The fact that this year’s Olympiad was the first of its kind greatly influenced our experience of participating in it. It differs from traditional international Olympiads like IMO and IOI, which have a large international community and a long history of training participants. We went to the competition without knowing what tasks we would face, who the other participants would be, or how the results would be checked, etc. We prepared using official materials, but clearly it wasn’t enough.
Many teams that did not win medals included participants or candidates for the IOI and IMO national teams. These are extremely smart people who, unfortunately, did not dedicate enough time to studying the features of ML. I am sure that next year it will be even more challenging but also more interesting to participate in this Olympiad.
I am pleased to have taken first place, although it was a difficult achievement. We have been diligently preparing for a long time and we have benefited from years of experience in Olympiads, including the All-Russian Olympiad for schoolchildren, as well as our knowledge of mathematics and computer science.
About machine learning
— I've been interested in machine learning for a long time, but I didn't acquire the skills I needed to study it until the 10th grade. Even a basic understanding of ML methods requires a good knowledge of mathematics at a level beyond the school curriculum. At first, ML seemed like magic to me, and I tried unsuccessfully to play around with existing models. It wasn’t until grades 10–11 that I started to study machine learning more seriously. I read articles and books about ML, worked on small projects.
About HSE University
— I have realised that HSE University meets my requirements much better than other universities. This decision was based on several factors, including university values, campus infrastructure, teaching staff, and study process. I am confident that the ‘Applied Mathematics and Information Science’ programme at FCS offers the best bachelor's programme in computer science in Russia.
About my future plans
— In the next four years, my main focus will be on completing my degree. I may participate in student competitions or internships, but studying remains my top priority. After graduating, I hope to pursue a master’s degree in a related field. I envision myself engaging in scientific research and perhaps working in the ML field. While it’s difficult to predict what my interests will be in a few years, at the moment, I can only speak about my current interests, hoping they will remain relevant in the future.
Andrey Gritsaev
About the Olympiad
— I wanted to participate in the Olympiad for several reasons. Firstly, it was an opportunity to compete and solve problems in a field that I’m interested in. Secondly, I wanted to meet smart students from other countries who shared my passion for AI. The Olympiad consisted of two rounds: a scientific round and a practical round. In the scientific round, we were given problems that simulated real research in AI. These problems were divided into three categories: machine learning, natural language processing, and computer vision. During the practical round, we conducted experiments with AI software, such as ChatGPT and DALL-E 2.
Tips for high school students
— Consider participating in the All-Russian Olympiad in Artificial Intelligence if you are interested in this field. Additionally, if you want to learn more about machine learning, I suggest taking some courses. This year, there may be training camps preparing for AI Olympiads, so it's worth keeping an eye out for updates from the Olympiad community.
About my future plans
— In the next few years, I intend to pursue research and work in this area.
See also:
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).
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.