Megascience, AI, and Supercomputers: HSE Expands Cooperation with JINR
Experts in computer technology from HSE University and the Joint Institute for Nuclear Research (JINR) discussed collaboration and joint projects at a meeting held at the Meshcheryakov Laboratory of Information Technologies (MLIT). HSE University was represented by Lev Shchur, Head of the Laboratory for Computational Physics at the HSE Tikhonov Moscow Institute of Electronics and Mathematics (HSE MIEM), as well as Denis Derkach and Fedor Ratnikov from the Laboratory of Methods for Big Data Analysis at the HSE Faculty of Computer Science.
One of HSE’s key initiatives at JINR will be the application of artificial intelligence and machine learning algorithms—developed by researchers at the HSE Faculty of Computer Science—to analyse experimental data generated at the NICA accelerator complex.
NICA (Nuclotron-based Ion Collider fAcility) is a proton and heavy-ion collider located in Dubna, designed to study the properties of dense baryonic matter. This accelerator complex enables scientists to recreate, in laboratory conditions, states of matter similar to those that existed in the first moments after the Big Bang.
Denis Derkach
‘NICA is an example of a megascience project—one of the largest international research facilities. Our participation reaffirms that HSE operates at the highest level of scientific research. Partnering with JINR, one of the world’s leading centres for particle physics, will significantly expand our research scope and attract talented young scientists to tackle pressing scientific challenges,’ said Denis Derkach, Director for Applied Research and Development of the HSE AI and Digital Science Institute and Head of the Laboratory of Methods for Big Data Analysis at the HSE Faculty of Computer Science.
According to Fedor Ratnikov, Leading Research Fellow at the HSE Laboratory of Methods for Big Data Analysis, both HSE and the Meshcheryakov Laboratory of Information Technologies (MLIT) are working towards the same goal—advancing cutting-edge physics research through state-of-the-art data processing and analysis methods.
Fedor Ratnikov
‘While MLIT is primarily focused on practical applications, our expertise lies in developing methodologies and approaches. This means we naturally complement each other in tackling this large-scale challenge,’ Fedor Ratnikov emphasised.
Another key area of collaboration will be distributed computing and the efficient utilisation of HSE’s cHARISMa HPC cluster and JINR’s Govorun supercomputer to solve complex problems in statistical physics.
Lev Shchur
‘We have long collaborated with our colleagues at the Joint Institute for Nuclear Research and were invited by Vladimir Korenkov, the Scientific Leader of the Meshcheryakov Laboratory of Information Technologies. We have developed unique algorithms capable of solving complex problems in statistical physics, which can also be applied to optimisation tasks. Our software solutions can fully utilise the power of supercomputers, which will undoubtedly benefit JINR’s scientific research, including in applied fields such as modern IT applications in agriculture and radiobiology. Additionally, we are also really interested in knowledge exchange on supercomputer optimisation and monitoring,’ shared Lev Shchur.
A vital aspect of the HSE–JINR partnership is the involvement of students in high-level research projects. The HSE Faculty of Computer Science and MIEM train specialists proficient in modern data analysis methodologies. However, hands-on experience with cutting-edge technologies can only be gained through participation in real-world projects. The collaboration with JINR provides students with the opportunity to engage in large-scale international megascience projects, such as NICA experiments at the Joint Institute for Nuclear Research.
As part of this partnership, researchers have agreed to hold a joint scientific session at the 11th International GRID'2025 Conference, scheduled for July 2025. During this session, participants will review and compare results from specific scientific tasks undertaken within the framework of HSE–JINR joint projects.
See also:
AI vs AI: Scientists Develop Neural Networks to Detect Generated Text Insertions
A research team, including Alexander Shirnin from HSE University, has developed two models designed to detect AI-generated insertions in scientific texts. The AIpom system integrates two types of models: a decoder and an encoder. The Papilusion system is designed to detect modifications through synonyms and summarisation by neural networks, using one type of models: encoders. In the future, these models will assist in verifying the originality and credibility of scientific publications. Articles describing the Papilusion and AIpom systems have been published in the ACL Anthology Digital Archive.
HSE Researchers Develop Python Library for Analysing Eye Movements
A research team at HSE University has developed EyeFeatures, a Python library for analysing and modelling eye movement data. This tool is designed to simplify the work of scientists and developers by enabling them to efficiently process complex data and create predictive models.
‘Many Want to Create AI-Based Products and Become More Competitive’
In 2024, the online Russian-taught master’s programme ‘Artificial Intelligence,’ offered by the HSE Faculty of Computer Science, saw a record number of first-year students—over 300. What accounts for such a high interest in AI, how the curriculum is structured, and what new skills will graduates acquire? Elena Kantonistova, the programme’s academic director, shares more.
'I Would Like to Leave a Lasting Impact on Science'
Aibek Alanov pursues his own scientific research and leads two teams of scientists, one at HSE University and the other at AIRI. In this interview for the HSE Young Scientists project, he explores the parallels between today's AI researchers and early 20th-century physicists, discusses generative models, and shares his passion for bachata partner dancing.
HSE’S Achievements in AI Presented at AIJ
The AI Journey international conference hosted a session led by Deputy Prime Minister Dmitry Chernyshenko highlighting the achievements of Russian research centres in artificial intelligence. Alexey Masyutin, Head of the HSE AI Research Centre, showcased the centre’s key developments.
Drivers of Progress and Sources of Revenue: The Role of Universities in Technology Transfer
In the modern world, the effective transfer of socio-economic and humanities-based knowledge to the real economy and public administration is essential. Universities play a decisive role in this process. They have the capability to unite diverse teams and, in partnership with the state and businesses, develop and enhance advanced technologies.
AI on Guard of Ecology: Students from Russia and Belarus Propose New Solutions to Environmental Problems
An international online hackathon dedicated to solving environmental problems was held at HSE University in Nizhny Novgorod. Students employed artificial intelligence and computer vision to develop innovative solutions for image segmentation, predictive modelling (forecasting future events based on data from the past) of emissions and creating chatbots for nature reserves and national parks.
Taming the Element: How AI Is Integrating into the Educational Process Around the World
Artificial intelligence is gradually becoming an indispensable part of higher education. Both students and teachers use it to reduce the volume of routine tasks and expand their capabilities. The limitations and prospects of AI are discussed in the report ‘The Beginning of the End or a New Era? The Effects of Generative Artificial Intelligence (GAI) in Higher Education,’ published in the journal Modern Education Analytics, under the scientific supervision of HSE Academic Supervisor Yaroslav Kuzminov.
A New Tool Designed to Assess AI Ethics in Medicine Developed at HSE University
A team of researchers at the HSE AI Research Centre has created an index to evaluate the ethical standards of artificial intelligence (AI) systems used in medicine. This tool is designed to minimise potential risks and promote safer development and implementation of AI technologies in medical practice.
HSE Researchers Develop Novel Approach to Evaluating AI Applications in Education
Researchers at HSE University have proposed a novel approach to assessing AI's competency in educational settings. The approach is grounded in psychometric principles and has been empirically tested using the GPT-4 model. This marks the first step in evaluating the true readiness of generative models to serve as assistants for teachers or students. The results have been published in arXiv.