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Regular version of the site

109028, Moscow,
11, Pokrovsky boulevard

Phone: +7 (495) 531-00-00 *27254

Email: computerscience@hse.ru


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
A randomized coordinate descent method with volume sampling

Rodomanov A., Kropotov D.

SIAM Journal on Optimization. 2020. Vol. 30. No. 3. P. 1878-1904.

ML-assisted versatile approach to Calorimeter R&D

A. Boldyrev, D. Derkach, F. Ratnikov et al.

Journal of Instrumentation. 2020. Vol. 15. P. 1-7.

An accelerated directional derivative method for smooth stochastic convex optimization

Dvurechensky P., Eduard Gorbunov, Gasnikov A.

European Journal of Operational Research. 2021. Vol. 290. No. 2. P. 601-621.

Book chapter
On pattern setups and pattern multistructures

Kuznetsov S., Kaytoue M., Belfodil A.

In bk.: International Journal of General Systems. Iss. 49. 2020. P. 271-285.

Book chapter
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise

Kaledin M., Moulines E., Naumov A. et al.

In bk.: Proceedings of Machine Learning Research. Vol. 125: Proceedings of Thirty Third Conference on Learning Theory. 2020. P. 2144-2203.

Colloquium: "Is the next deep learning disruption in the physical sciences?"

We invite everyone to the 100th anniversary colloquium of the CS department. For such an occasion, we invited a special guest - Max Welling from the University of Amsterdam.

When: 26 October, 16:20 

Title: Is the next deep learning disruption in the physical sciences


A number of fields, most prominently speech, vision and NLP have been disrupted by deep learning technology. A natural question is: "which application areas will follow next?".  My prediction is that the physical sciences will experience an unprecedented acceleration by combining the tools of simulation on HPC clusters with the tools of deep learning to improve and accelerate this process. Together, they form a virtuous cycle where simulations create data that feeds into deep learning models which in turn improves the simulations. In a way, this is like building a self-learning computational microscope for the physical sciences. In this talk I will illustrate this using two recent pieces of work from my lab: molecular simulation and PDE solving. In molecular simulation we try to predict molecular properties or digitally synthesize molecules with prescribed properties. We have built a number of equivariant graph neural networks to achieve this. Partial differential equations (PDEs) are the most used mathematical model in natural sciences to describe physical processes. Intriguingly, we find that PDE solvers can be learned from data using graph neural networks as well, which has the added benefit that we can learn a solver that can generalize across PDEs and different boundary conditions. Moreover, it may open the door to ab initio learning of PDEs directly from data.

About the speaker: 

Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft.

Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).


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