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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
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.

Book chapter
Re-pairing brackets

Chistikov D., Mikhail Vyalyi.

In bk.: LICS '20: 35th Annual ACM/IEEE Symposium on Logic in Computer Science. Saarbrücken, Germany. July, 2020. Association for Computing Machinery (ACM), 2020. P. 312-326.

Magnetohydrodynamic Modeling of the Solar Wind Key Parameters and Current Sheets in the Heliosphere: Radial and Solar Cycle Evolution

E. V. Maiewski, Kislov R. A., Khabarova O. V. et al.

Astrophysical Journal. 2020. Vol. 892. No. 1. P. 1-17.

Influence of Very Large Spatial Heterogeneity on Estimates of Sea-Level Trends

Shapoval A., Le Mouël J., Courtillot V. et al.

Applied Mathematics and Computation. 2020. Vol. 386. P. 125485.

Sixth Summer School on Machine Learning in High Energy Physics is completed

Sixth Summer School on Machine Learning in High Energy Physics is completed

Laboratory of Methods for Big Data Analysis (LAMBDA) of the Faculty of Computer Science, HSE University held the sixth summer school on Machine Learning in High Energy Physics (MLHEP) on July 16-30. The school was organised in cooperation with Yandex School of Data Analysis and EPFL High Energy Physics Laboratory. This year the school was listed as an official EPFL course with 4 ECTS credits awarded to the participants successfully passing the school requirements.

The initial plan was to hold the school at the EPFL campus (Lausanne, Switzerland); however, due to pandemic, the school was organised online for the first time. The school team faced several challenges in distributing the content and communicating with students. In the end, the team utilised a combination of OBS Studio, Screencast-O-Matic, and iPad built-in tools. The content was uploaded to the novel Pelican teaching platform (https://en.pelican.study/) that supports various types of materials and student performance evaluation. 


The school curriculum spanned over the following sections:

  1. Introduction into Machine Learning
  2. Introduction into Deep Learning
  3. Bayesian Deep Learning
  4. Generative models
  5. Optimisation methods
  6. Advanced topics

In addition, we have invited brilliant guest speakers from both Machine Learning (DeepMind, Facebook AI Research, IBM Research, MIT, Oxford) and HEP fields (INFN, SLAC, Yale University). 

Michela Paganini
Facebook AI Research

«The school covered a compelling breadth of topics that guided students along a learning path from elementary to advanced notions in machine learning. Building on the extensive experience of HEP as a global and decentralised research community, the organisers guaranteed a smooth and seamless experience for us as speakers, as we delivered our lectures live on videoconference. Kudos to everyone involved!»

Within each the sections 1-5, the time has been divided evenly between lectures and seminars. Most of the lectures have been pre-recorded, and the Q&A session was moved to the seminars. Due to the online format, the biggest challenge for the seminar was to allow effective communications with 100+ students during the interactive part. We had to deploy a CoCalc platform for online teaching. It has built-in JupyterLab functionality with the ability to share notebooks like Google Docs. At the same time, the platform has fantastic potential and flexibility to use GPU resources and to perform student assessment distribution and evaluation. Although it didn't work smoothly out of the box for us, with the help of our team we've made it run successfully in the end.

Thanks to the online format, the school has hosted twice as many students than usually. This year we shared our program with one hundred and fourteen participants, representing twenty-three countries and seventy-three universities. The map shows the geography of our participants.

Participants' Geography
Participants' Geography

Lara Mason
PhD student from the University of Johannesburg and the University of Lyon

”I'm really enjoying the school (it took me a few days to get used to the pace, as I am not a super-strong coder, so I am a little slower on lots of the tasks, but I think I am up to speed now). Everyone has been so helpful and friendly - especially using the Cocalc chat in help debugging. I really appreciated that. I can see that a lot of effort is going into running the program and I would like to say a huge thank you for that!"

Sara Celani
Second-year PhD student from EPFL

”I was happy about the lectures, but I missed the social contact with other people and the exchanging of thoughts and opinions. Anyway, I think that organising such a thing in the whole online format was not an easy task, so I also appreciate all the work you have done to make it possible also during these difficult times."

Andrey Ustyuzhanin
MLHEP school director and head of LAMDA lab

"We are charmed with the results of the school, and we plan to release some of the school materials later this year. There is plenty of feedback to analyse and plenty of room for further improvements including finding approaches for better socialisation between the participants during online events like this."

Lesya Shchutska

“I believe the school came out as a huge challenge both for the lecturers to have it fully reshaped in a new format, and, what is more important, for the students, who needed to experience full immersion for two weeks to get it going for them. But as they say, what does not break you - makes you stronger. And I hope this proverb worked out for our highly motivated audience!”

We are very thankful to our partners Yandex and SIT, as well as our sponsors EPFL and IBM Research Zurich for making it happen. The next iteration of the school is planned to be at EPFL in summer 2021. We plan to run it in a blended online-offline format, mixing strengths of both.