HSE and Yandex Hold Second Summer School in Machine Learning in High Energy Physics
On June 20-27, the HSE Faculty of Computer Science teamed up with the Yandex School of Data Analysis to carry out the Second International Summer School in Machine Learning in High Energy Physics. This year’s Summer School took place in Lund, Sweden, and saw the participation of 60 students and researchers from Austria, Great Britain, Germany, Italy, Russia, the U.S., Sweden, Switzerland, and more. Of these students, five came from the Higher School of Economics.
The Summer School’s programme offered two tracks – basic and advanced. The basic track focused on key topics needed to build classification models for data collected using modern-day particle detectors. Lecturers included Alexey Rogozhnikov, while practicums were held by Nikita Kazeev, Tatiana Likhomanenko, and Alexander Panin. The advanced track included topics such as particle trajectory restoration (Mikhail Guschin), methods for optimising real-time data selection algorithms (Tatiana Likhomanenko), and joint research technologies (Andrey Ustyuzhanin). Both tracks then turned into a discussion of how in-depth learning can be applied to physics experiments (lecture by Alexander Panin; practicums by Tatiana Likhomanenko and Alexey Rogozhnikov). To strengthen the data analysis methods covered at the lectures and seminars, two competitions were also organised on the Kaggle platform. The advanced tract worked on improving event filters, while the basic track worked on the search for decays of the Higgs boson. There were many physicists and machine-learning experts from all over the world invited to speak at the Summer School: Chase Shimmin (UC Irvine, U.S.), Gilles Louppe (NYU, U.S.), Vicens Gaitan (Grupo AIA R&D, Spain), Joaquin Vanschoren (TUE, the Netherlands), Michael Williams (MIT, U.S.). Two computer clusters were used at the school, the first of which was provided by Yandex (300 cores, 1.5 terabytes of memory), while the second came from a Finnish supercomputer centre (48 GPU cards).
Petr Zhizhin, First-year Applied Mathematics and Informatics Student
‘The trip to Sweden began when our student group got an invitation in the mail. I filed out the form, which asked about the different programming languages I knew, what I expected from my school, and why I should be selected for the event.
I’ve loved physics since I was in elementary school. It was one of the most difficult subjects available, and it always posed a challenge for me with all of the complex tasks it entails. Each year I participated in different physics Olympiads. I got a hundred on the Unified State Exam and even participated in the Faculty of Computer Science’s PhysTech Olympiad in physics.
So I simply had to apply for the School. It was an excellent opportunity for me to meet real physicists who work in the field of machine learning. When I got the call that I’d been accepted, I was beside myself with joy.
Classes at the School were taught by staff from the LAMBDA laboratory who work both within the faculty and at Yandex. It was a lot of work. I didn’t sleep more than five hours a single night of the Summer School. Over the course of a week, we looked at various algorithms such as linear regression, decision trees, random decision forests, boosting algorithms, and neural networks. After completing the School, I knew exactly which areas of mathematics I needed to study in the future.
For our track, the organisers selected a project dealing with machine learning in high-energy physics – the search for decays of the Higgs boson. To complete our task, we tried out a large number of different machine-learning algorithms. They didn’t all work out that well, but the main steps included random decision forests, XGBoost, XGBoost with Grid Search, and neural networks. I can definitely say that I was surprised when the neural networks yielded significantly better results than the other models.
After the Summer School, the instructors said that they were really impressed by our effort and that we were the youngest team at the school. They took note of the results we achieved in the competition and gave us commemorative prizes.’
In addition to Petr Zhizhin, a first-year student in the Applied Mathematics and Informatics programme, the Summer School also saw the participation of third-years Alexander Tiunov and Roman Schedrin of the Applied Mathematics and Informatics Programme, Ekaterina Rubtsova from the Software Engineering programme, and Artem Filatov from the Economics programme.
On July 17-23 the Third Machine Learning summer school organized by Yandex School of Data Analysis, Laboratory of Methods for Big Data Analysis at the National Research University Higher School of Economics and Imperial College London was held in Reading, UK. 60 students, doctoral students and researchers from 18 countries and 47 universities took part in the event.
The Summer School ‘Law in Russia: National Aspects’ was held at HSE Nizhny Novgorod on July 17-28. Students from HSE Nizhny Novgorod and the Southwest University of Political Science and Law (Chongqing, China) took part in the event. For two weeks the international participants studied the basics of Russian language, law, and management.
In July 2017, the HSE Institute of Education welcomed its first international summer school 'Inequality of Educational Opportunities'. Organized by the IOE International Laboratory for Education Policy Analysis, this event aimed to promote best-practice approaches to inequality research through multi-dimensional academic debate and learning about today’s advanced methodology in social data analysis.
The 6th International Summer School on Cyber Law has come to a close in Moscow. The event was organized by the HSE International Laboratory for Information Technology and Intellectual Property Law. The school is conducted in English. Young researchers from Austria, Belarus, Germany, Great Britain, India, Mongolia, Spain, Poland, Russia, and Ukraine presented their papers at this annual event.
The VII International Russian-Chinese Summer School on International Relations ‘Economic Instruments of Foreign Policy in the Modern World’ was held on July 6-13 at the HSE Faculty of World Economy and International Affairs (Moscow) in full cooperation with the East China Normal University (Shanghai) and with the support of the Gorchakov Fund.
In mid-June 2017, the town of Pushkin near St. Petersburg, Russia welcomed the Fifth International Summer School on Higher Education Research, a joint initiative between the HSE Institute of Education and Peking University’s China Institute for Educational Finance Research. This year, the Summer School focused on higher education and social inequality.
The HSE Vysokovsky Graduate School of Urbanism together with the French association D’est are organizing a joint summer school this year in France. The School will run from June 25 – July 8, 2017 in Paris and Montpellier and aims to attract students and young professionals in urban development and planning, municipal administration, and civil initiatives.
Team HSE has taken second place at RuCTF - a leading information security competition. The championship was held in Ekaterinburg on April 14-17, 2017. RuCTF (‘Capture the Flag’) is an annual open all-Russian interuniversity competition and conference on information security. The event has been held annually since 2008.
Thirty school students from Moscow and the Moscow Region recently had an opportunity to meet international researchers and analyze data obtained from the Large Hadron Collider at a workshop organized by HSE’s Faculty of Computer Science, Yandex and CERN.
Sergey Shershakov is 2012 graduate of the HSE master’s programme in System and Software Engineering, lecturer of a course in Data Algorithms and Structures, a researcher at the Laboratory of Process-Aware Information Systems (PAIS Lab), and participant of the Young Faculty Support Programme in the Category ‘New Researchers’. Sergey told us what Process Mining is, how to keep your knowledge up-to-date without working in the industry, and why HSE graduates don’t have to ‘forget everything they’ve been taught’.