Tag "machine learning"

Based on the results of a project competition, two new laboratories are opening at HSE University’s Faculty of Computer Science. The Laboratory for Matrix and Tensor Methods in Machine Learning will be headed by Maxim Rakhuba, Associate Professor at the Big Data and Information Retrieval School. The Laboratory for Cloud and Mobile Technologies will be headed by Dmitry Alexandrov, Professor at the School of Software Engineering.

One of the winning projects of a competition held by HSE University’s Mirror Laboratories last June focuses on the use of machine learning technologies to predict the outcomes of acute coronary syndrome. It is implemented by HSE University’s International Laboratory of Bioinformatics together with the Research and Educational Centre of the Medical Institute at Surgut State University. Maria Poptsova, Head of the International Laboratory of Bioinformatics and Associate Professor at HSE University’s Faculty of Computer Science, talks about how this joint project originated, how it will help patients, and how work to implement it will be organised.

On October 26–28, HSE University’s Institute of Artificial Intelligence and Digital Sciences at the Faculty of Computer Science will hold a conference titled Fall into ML 2023 withsupport from the HSE University AI Research Centre . The event is dedicated to promising directions of fundamental AI development.

Researchers of the HSE Graduate School of Business trained a machine-learning (ML) model to infer users' subjective wellbeing from social media posts. Having processed 10 million tweets, the researchers compiled a rating of holidays celebrated in Russia based on their popularity. The New Year tops the list, but Russian-speaking users of Twitter are also happy to celebrate Defender of the Fatherland Day, International Women's Day, and Halloween. The study findings have been published in PeerJ Computer Science.

A group of researchers from the HSE Faculty of Computer Science and the Sber AI Lab has increased the speed of gradient boosting, one of the most efficient machine learning algorithms. The proposed approach will make it possible to solve classification and regression problems faster. The results of the work were presented at the NeurIPS conference.

Researchers from HSE University and the Russian Technological University (RTU MIREA) have developed an intelligent system to automate software testing on a variety of platforms. Its computer vision feature is capable of recognising elements in a graphical user interface even after a redesign. The details are published in the Journal of the Siberian Federal University.

A team of researchers from HSE MIEM, LPI RAS, and the University of Southern California have applied machine learning to the analysis of internal defects in perovskite solar cells and proposed ways to improve their energy efficiency. The findings of the study performed on the Cs₂AgBiBr₆ double perovskite can be used to develop more efficient and durable perovskite-based materials. The paper has been published in the Journal of Physical Chemistry Letters.

How can the metaverse help to improve public administration? Can gamification change attitudes to data? And how can governments create ethical data policies? On June 29–30 2022, the International Laboratory for Digital Transformation in Public Administration held an online conference dedicated to these questions and more. The conference was organised and moderated by Laboratory Head Evgeny Styrin and Leading Research Fellow Anna Sanina.

A research team from the HSE University Artificial Intelligence Centre led by Ivan Yamshchikov has developed a model to predict the success of efforts to rehabilitate homeless people. The model can predict the effectiveness of the work of organisations for the homeless with about 80% accuracy. The project was presented at a conference dedicated to the activities of social centres.

HSE scientists Andrey Korotayev and Ilya Medvedev used machine learning (ML) to build an index of instability in the world. The new method made it possible to use a large number of variables and distribute them in non-standard fashion.