Dean — Ivan Arzhantsev
First Deputy Dean — Tamara Voznesenskaya
Deputy Dean for Research and International Relations — Sergei Obiedkov
Deputy Dean for Methodical and Educational Work — Ilya Samonenko
Deputy Dean for Development, Finance and Administration — Irina Plisetskaya
Phone: +7 (495) 772-95-90 * 12332
125319, Moscow, 3 Kochnovsky Proezd (near metro station 'Aeroport').
Bienvenu M., Kikot S., Kontchakov R. et al.
Journal of the ACM. 2018. Vol. 65. No. 5. P. 28:1-28:51.
Doikov Nikita, Richtarik P.
Proceedings of Machine Learning Research. 2018. No. 80. P. 1290-1298.
Hushchyn M., Chekalina V.
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2018. P. 1-2.
Naumov A., Spokoiny V., Ulyanov V. V.
Probability Theory and Related Fields. 2019. P. 1-42.
Shapoval A., Le Mouël J., Shnirman M. et al.
Astronomy and Astrophysics. 2018. Vol. 618. P. A183-1-A183-13.
The faculty trains developers and researchers. The programme has been created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). Also taken into consideration when creating the faculty was the School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. The wide range of elective courses will allow each student to create his or her own educational path. In the faculty, learning is based on practice and projects.
Head of vision modelling lab / HSE
Computational models in psychology and neuroscience share many algorithms with machine learning, machine vision and artificial intelligence, but the focus of the research is different. Where applied fields try to create algorithms that solve or automate a specific problem, computational modelling uses these algorithms to better understand fundamental workings of human brain and cognition. Rather than optimizing a new process, we try to simulate and understand an existing process. While computational modelling is still a growing field, there have emerged a number of contenders that perform very well in simulating various neural and cognitive processes. Diffusion models of decision making, salience models of vision and more recently deep learning models of object classification have all shown promise on their respective tasks. This talk will give an overview of a number of these models and discuss possible points of overlap with computer science and cognitive psychology.