Bayesian methods research group is located in three places: Skoltech, faculty of Computer Science of National research institute Higher School of Economics, Moscow and Computational Mathematics and Cybernetics Department of Moscow State University. It consists of 3 postgraduates (including one PhD), 5 PhD students, and 10 students. We carry out research work on the development of new machine learning and Bayesian inference algorithms which take into account the specific features of given problem. We widely exploit Bayesian framework and the theory of graphical models in particular.

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 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 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 Yandex, NVidia, Kaspersky lab.

An important direction of our work are applied projects from many domains including text processing, computer vision, software code analysis. During the work over the projects the 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 teaching process in Moscow State University, Higher School of Economics, and Skoltech.

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