The LAboratory of Methods for Big Data Analysis (LAMBDA) currently focuses on using machine-learning and data analysis methods to solve issues in fundamental sciences such as particle physics and astrophysics. The Laboratory’s main developmental direction is to work with leading scientists from these fields to search for answers to the universe’s mysteries. Specifically, the Laboratory cooperates with the European Organization for Nuclear Research (CERN), researching the events of the Large Hadron Collider and raising the efficiency of data analysis.
In addition, the Laboratory’s educational activities include organizing and carrying out academic seminars and summer/winter schools on big data analysis and providing scientific guidance to thesis and dissertation work.
LAMBDA was created in February 2015.
‘We Facilitate High-Speed Car Crashes and Study How Car Engines Work Based on Photos of Flying Debris’
Nikita Kazeev holds a Candidate of Sciences degree (Russian equivalent of a PhD) in Computer Science and a PhD in Physics. He is a Research Fellow at the LAMBDA Laboratory and works at CERN. In an interview with HSE News Service, he talked about what it was like to defend his dissertation in a double doctoral degree programme at HSE University and Sapienza University of Rome, what it is like to conduct research in Geneva, and why it is imperative to communicate with colleagues.
Mikhail Guschin, Research Fellow at the HSE University Laboratory of Methods for Big Data Analysis of the Faculty of Computer Science, was appointed coordinator of the machine learning and statistics working group in the LHCb Large Hadron Collider experiment at CERN (the European Organization for Nuclear Research). He will be the only representative of a Russian University among the coordinators for the experiment’s working groups.
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.
𝐋𝐀𝐌𝐁𝐃𝐀 scientists Andrey Ustyuzhanin and Denis Derkach have participated as speakers at the Open conference on artificial intelligence “𝐎𝐩𝐞𝐧𝐓𝐚𝐥𝐤𝐬.𝐀𝐈”
Tracking Machine Learning challenge (TrackML) has been organized throughout 2018 and 2019 by a team of particle physicists and tracking experts to reach out to Computer Science and Machine Learning expertise of the wider community.
The Higher School of Economics has joined the LHCb collaboration at the Large Hadron Collider, which is operated by the European Organization for Nuclear Research. The group from HSE will consist of researchers from the Laboratory of Methods for Big Data Analysis (LAMBDA). This will give HSE researchers full access to data from the collaboration and allow the university to participate in various projects.
The cost of SHiP detector to be reduced by 25%
National Research University Higher School of Economics hosted a masterclass in particle physics on March 27 and brought high school students to participate. These students took a day off from school to go to the University and dive into the actual data. Scientists of the HSE Laboratory of Methods for Big Data Analysis (LAMBDA) Denis Derkach and Fedor Ratnikov introduced them to the tiniest building blocks of the universe and to the accelerators and detectors which probe these mysterious particles.
There is a reception of applications for the fourth summer school "Machine Learning in High Energy Physics"
The Fourth Machine Learning summer school (MLHEP 2018) will be held in Oxford, UK from 6 to 12 August 2018. https://indico.cern.ch/event/687473/
A team of researchers from the HSE Laboratory of Methods for Big Data Analysis (LAMBDA) has won a contest held by the Presidential Research Funding Programme. Researchers with the laboratory are developing a system of algorithms that will help physicists look for new particles in the Large Hadron Collider.