Research & Expertise

Mathematicians at HSE University–Nizhny Novgorod, in collaboration with colleagues from Tongji University in Shanghai, are investigating the fundamental causes of structural stability in systems and the mechanisms underlying its disruption. In this interview with the HSE News Service, Prof. Olga Pochinka, Head of the International Laboratory of Dynamical Systems and Applications at HSE University–Nizhny Novgorod and leader of the project ‘Qualitative Theory of Systems of Ordinary and Partial Differential Equations,’ discusses the project, which is being implemented as part of HSE University's International Academic Cooperation programme.

Researchers at the HSE Centre for Language and Brain took part in a rare awake neurosurgical procedure performed on an 11-year-old patient with drug-resistant epilepsy. Working alongside surgeons at the Voyno-Yasenetsky Centre of Specialised Medical Care for Children in Solntsevo, they monitored the resection of a portion of the left temporal lobe, where the epileptic focus had been identified.

Today, interactions between citizens and government increasingly take place through digital governance platforms, including digital public services, AI-powered systems, and algorithmic decision-making tools. Until now, however, these technologies have largely been viewed as technical instruments, with their effectiveness assessed primarily in terms of efficiency and user-friendliness. The authors of a new study propose a broader perspective, arguing that digital governance should also be understood as an emotional experience that directly shapes citizens' trust in public institutions.

Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.

At the 2026 St Petersburg International Economic Forum, participants examined the global economy, trade and logistics in turbulent times, as well as the role of pension savings in Russia. Here are highlights from the sessions featuring HSE University experts.

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.

Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.

Artificial intelligence (AI) significant impacts on the economy and society, touching on a wide range of areas, from strategic management and decision-making to everyday communication. Experts from the HSE Institute for Statistical Studies and Economics of Knowledge (ISSEK) used the iFORA system to map key fractures —areas where changes have already become irreversible and will only intensify.

At HSE University St Petersburg International Partners' Week, representatives of world-leading universities discussed how to adapt to the rapid development of AI technologies: from integrating neural networks into the educational process to reconsidering the role of the person in creative professions.

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.