Tag "machine learning"

Participants of the 4th Fall into ML conference at HSE University held a discussion titled ‘Academia in Crisis: What Does the Future Hold?’ In particular, they examined why the number of scientific publications continues to grow, what the quality of these papers is, what expectations should be placed on researchers, and what role artificial intelligence plays in preparing academic articles.

Participation in high-level international events is crucial for the professional development of researchers starting their career in science. It provides an opportunity to exchange ideas, create promising teams, and make important connections. This summer, Denis Ryapolov and Askar Tsyganov, students of the Applied Mathematics and Computer Science programme and staff of the AI and Digital Sciences Institute at the Faculty of Computer Science of HSE University, attended the prestigious international Conference on Learning Theory, COLT 2025, which was held in Lyon, France. In an interview, they shared their experiences and impressions from the trip.

Researchers at HSE University have developed a machine learning (ML) model capable of predicting the risk of complications—major adverse cardiac events—in patients following a myocardial infarction. For the first time, the model incorporates genetic data, enabling a more accurate assessment of the risk of long-term complications. The study has been published in Frontiers in Medicine.

On October 25–26, 2024, HSE University’s AI and Digital Science Institute and the AI Research Centre hold the Fall into ML 2024 conference in Moscow. This year’s event will focus on the prospects in development of fundamental artificial intelligence, with SBER as its conference title partner.

Inflation is a key indicator of economic stability, and being able to accurately forecast its levels across regions is crucial for governments, businesses, and households. Tatiana Bukina and Dmitry Kashin at HSE Campus in Perm have found that machine learning techniques outperform traditional econometric models in long-term inflation forecasting. The results of the study focused on several regions in the Privolzhskiy Federal District have been published in HSE Economic Journal.

HSE scientists have developed a software tool for assessing the presence and degree of dyslexia in school students based on their gender, age, school grade, and eye-tracking data. The application is expected to be introduced into clinical practice in 2024. The underlying studies were conducted by specialists in machine learning and neurolinguistics at the HSE AI Research Centre.