Bayesian Methods Research Group

Recent years have proved that the more data is involved into analysis, the better (often much better) results one may obtain. The breakthrough in machine learning has happened due to the successful application of deep neural networks which turned out to be extremely powerful when dealing with huge amounts of data. However, it is now clear that classical methods simply do not work when one needs to process extremely large datasets. So the New Mathematics or the mathematics of Big Data Age is needed. Our group is involved in the process of developing such mathematics and is carrying out research in deep learning, stochastic optimization, tensor decompositions, scalable variational inference. Our efforts are supported by Samsung, Yandex, NVIDIA, Kaspersky lab, Sberbank, Schlumberger, JetBrains.

Important directions of our work are applied projects from many domains including text processing, computer vision, software code analysis. During the work over the projects, students gain practical experience of using different algorithms from computer science as well as software engineering skills. We strongly encourage the research activity of students and the publication of papers authored or co-authored by students.

Our group is involved in the teaching process at Higher School of Economics, Yandex School of Data Analysis and Moscow State University.