News

On April 24, the VI Russian Summit and Awards on Digital Transformation – CDO/CDTO Summit & Awards 2025 – took place at the HSE Graduate School of Business campus on Shabolovka Street in Moscow. The event was organized by LBS International Conferences, HSE University, and the HSE Graduate School of Business.

The X5 Tech Sprint is a championship in algorithmic programming. A total of 808 school pupils and university students from over 200 educational institutions across Moscow took part. HSE ranked first among universities in the overall participant leaderboard, and a student from the Faculty of Computer Science (FCS) achieved the best individual result.

The 2025 Breakthrough Prize in Fundamental Physics has been awarded to the international collaborations of experiments at the Large Hadron Collider (LHC) at CERN, including the LHCb collaboration, in which researchers from HSE University have participated.

The IX All-Russian Scientific Student Conference has taken place at the Nizhny Novgorod campus of HSE University, bringing together almost 200 early-career researchers and experts from 14 cities across Russia, from Kaliningrad to Vladivostok, as well as representatives from the African continent.

Researchers at HSE University and the Institute of Petrochemical Synthesis of the Russian Academy of Sciences have discovered a way to control both the colour and brightness of the glow emitted by rare earth elements. Their luminescence is generally predictable—for example, cerium typically emits light in the ultraviolet range. However, the scientists have demonstrated that this can be altered. They created a chemical environment in which a cerium ion began to emit a yellow glow. The findings could contribute to the development of new light sources, displays, and lasers. The study has been published in Optical Materials.

In biomedical research, machine learning algorithms are often used to analyse data—for instance, to predict cancer recurrence. However, it is not always clear whether these algorithms are detecting meaningful patterns or merely fitting random noise in the data. Scientists from HSE University, IBCh RAS, and Moscow State University have developed a test that makes it possible to determine this distinction. It could become an important tool for verifying the reliability of algorithms in medicine and biology. The study has been published on arXiv.

Artificial intelligence is transforming every aspect of life, expanding both our capabilities and our boundaries. At the same time, it presents new challenges for humanity, including concerns about safety, ethics, and environmental sustainability. Today, each neural network leaves a significant carbon footprint. However, with responsible management, AI has the potential to benefit the planet and become a cornerstone of a sustainable future economy. Panos Pardalos, Academic Supervisor of the Laboratory of Algorithms and Technologies for Network Analysis at the HSE Campus in Nizhny Novgorod, emphasised this point as he addressed the XXV Yasin (April) International Academic Conference on Economic and Social Development.

HSE researchers have developed an MLOps platform called SmartMLOps. It has been created for artificial intelligence researchers who wish to transform their invention into a fully-fledged service. In the future, the platform may host AI assistants to simplify educational processes, provide medical support, offer consultations, and solve a wide range of other tasks. Creators of AI technologies will be able to obtain a ready-to-use service within just a few hours. Utilising HSE’s supercomputer, the service can be launched in just a few clicks.

In March 2025, HSE Days took place in the Ghanaian capital of Accra, the cultural hub of Kumasi, and the Bono East region. Representatives of the university awarded participants of the HSE International Olympiad (HSE INTO 2025) and spoke to local school pupils and university students about educational opportunities, scholarship programmes, and application pathways to HSE University.

Researchers from HSE University in Nizhny Novgorod have shown that a neural network can reconstruct the dynamics of a brain neuron model using just a single set of measurements, such as recordings of its electrical activity. The developed neural network was trained to reconstruct the system's full dynamics and predict its behaviour under changing conditions. This method enables the investigation of complex biological processes, even when not all necessary measurements are available. The study has been published in Chaos, Solitons & Fractals.