‘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.
Double Doctoral Degree Programme
I met my HSE supervisor Andrey Ustyuzhanin at Yandex when I was studying at the School of Data Analysis. I was invited to intern with his group, which was engaged in machine learning for high energy physics at the European Organization for Nuclear Research (CERN). I met my dissertation advisor in Rome under rather unusual circumstances. The collider operates around the clock, and researchers visiting CERN do monitoring shifts. During their eight-hour shifts, the scientists keep track of how the experiments go. During one shift I met Barbara Sciascia, and later she invited me to the INFN Frascati laboratory.
In Russia I work at the LAMBDA laboratory. The lab’s main areas of research are based on the fact that machine learning methods have made a quantum leap in the past ten years. They can be used not only for already routine tasks, for example, to distinguish cats from dogs in photographs on social media, but also for more non-trivial ones, such as making discoveries in the natural sciences. The lab’s flagship area involves collaborating with the LHCb experiment at CERN, and this was the area in which I conducted my dissertation work. In addition, the laboratory optimized the design of the SHiP detector, computed evasive maneuvers for spacecraft, proposed a new black box optimisation at the leading NeurIPS conference, and, since 2015, they have been holding an annual summer school on machine learning.
Of course, the dual doctoral degree programme was not without problems: for example, unlike HSE, at the University of Rome it was not possible to defend a collection of articles instead of a dissertation manuscript. You had to write a full-fledged PhD dissertation, supplemented by classical standards with a manual on machine learning and the hardware of the LHCb experiment. They believe that a good researcher should be able to communicate their results to a reader who is not an expert in the field. In addition, HSE had its own peculiarities in terms of document flow, and as a result, my Italian supervisors had to send reviews of my dissertation by mail. On the other hand, in Rome they got acquainted with my work in detail and asked good and interesting questions. In addition, thanks to this programme, I increased my Italian vocabulary from 5 to 15 words.
The pandemic did not particularly affect my work. At CERN they traditionally work remotely due to the large number of collaborators in different countries. I was in constant communication with the Italian group when I was working on a joint project with them, and before the pandemic, I spent less than a month total in Rome, as the main place of work for everyone was CERN in Geneva. We had originally planned for my supervisors and committee members to meet for the defense in Moscow, but this idea had to be abandoned.
Roughly speaking, our work at the Large Hadron Collider is like this: we facilitate high-speed car crashes and, using photographs of flying debris, we study how car engines work. Before starting to draw conclusions about the physics, it is necessary to determine the types of components in the photographs. My work involves determining the types of particles in a detector using machine learning. In total, my dissertation included four projects.
The first is global particle identification. Different detector components collect different information about the particles passing through them. If we go back to the example of cars, then we basically use cameras from different angles with different light filters to shoot a ‘collision’. The information obtained allows us to determine the type of particle. To solve this problem, we developed an algorithm based on machine learning (CatBoost), which handles this task better than the previous solution based on a simple neural network.
The second task is the identification of muons. Among other charged particles, muons are especially interesting in that they have a high penetrating power—they can pass through calorimeters and iron sheets of absorbers. We developed an algorithm that can quickly determine if a particle was a muon.
Another project involves the use of machine learning on noisy data. While working on the problem of muon identification, we ran into a problem. Our algorithms were trained on real, non-synthetic data, so their labeling was inaccurate—not all particles that were identified as muons were actually muons. But for each of them, we knew the probability that the label was wrong. We developed a method to train machine learning algorithms on such data. Since noisy data is used not only in physics, but also in most applications of machine learning in the real world, we are now actively looking for where else a similar noise model has to be used. If you are reading this and you have suitable data, please feel free to write to me.
In addition, I am engaged with fast simulation of Cherenkov detectors. Simulated data is required to create and validate algorithms that parse detector data. To do this, we collide virtual particles on a virtual detector using a special computer program. As a result of such an experiment, we have accurate information about what actually happened. The problem with simulated data is that it requires large computing resources—about one second for one event, and tens of millions of these events are required. I built an algorithm based on generative adversarial networks (GANs) that simulates the response of a detector to particles passing through it. This approach is two to three orders of magnitude faster than the simulation described earlier.
Almost all of these projects are interesting because they serve as tools for fundamental research.
We don’t know in advance what new physics we will discover, or how and when it may come in handy
This may sound a little sad, but in fact the opposite is true: out of pure scientific curiosity, mankind has already acquired electricity, radio waves, semiconductors and many other achievements that surround us. Well, from the obvious benefits, in addition to being used in a specific LHCb experiment, the developed methods can form the basis of algorithms for other physics experiments.
Remote Dissertation Defense
Initially, the defense was planned as a big party, which would have brought together foreign committee members and the dissertation advisors, but because of the pandemic, this did not work out. On the other hand, the online format made it possible for a large number of my friends to attend, not all of whom could have come in person. We considered an option in which those who wish could meet in person, and the rest could attend online, but this proved to be technically difficult. There was also even an idea to conduct the defense at the Italian embassy, but, again, the pandemic made its own adjustments.
The defense itself passed predictably and according to plan. All the committee members sent their reviews in advance. I was pleasantly surprised by the quality of the discussion, questions, and suggestions. After the defense, we celebrated with a buffet reception and a live action role-playing game.
In conclusion, I would like to say that the main thing is to not give up when plans don’t work out (due to the pandemic), to love your work, and not to miss out on interacting with colleagues. Who knows—your future supervisor could be sitting next to you right now.
International students interested in joining HSE University can apply for full-tuition scholarships from the Russian government. Applications for the 2022/23 academic year are open from March 1–11. We spoke to HSE University doctoral students about their work and about how scholarships have helped them pursue their research goals.
‘Gaining Practical Knowledge and Learning to Do a Lot in a Short Time’—The Life of an HSE PhD Student
Milica Simonovic, from Belgrade, Serbia, is a doctoral student at the HSE University Faculty of Economic Sciences. She is currently working on her doctoral thesis on corporate governance and intellectual capital disclosure. We interviewed Milica to find out more about her thesis and her experience of the university’s application process for PhD students.
HSE University alumna Dr. Elena Podkolzina recently became an academic supervisor of the newly established master's programme in Economics and Economic Policy. Having been part of HSE University for nearly 25 years now, Dr. Podkolzina recalls her student years and shares her experience as deputy director of the Center for Institutional Studies.
Alaa N. Assaf is a doctoral student at HSE University’s Faculty of Law. In addition to doing research for his PhD thesis, he teaches undergraduates. Alaa has shared his thoughts on studying international law, life in Moscow, and what he misses about teaching in person.
HSE University continues to develop its new study format for students embarking on a research career: the Combined Master's-PhD track. This year, there will be 75 places for Master’s students on the track—two thirds more than last year. HSE Vice Rector Sergey Roshchin talks about the appeal of the combined-track option, how to enrol, and the achievements of last year’s applicants.
'When We Find Something That Doesn’t Work Well Enough, We Replace It in Order to Develop a More Effective Approach'
The winners of the third annual Ilya Segalovich Award were recently announced in Moscow. Established by Yandex, the award promotes the scientific endeavours of young researchers from Russia, Belarus, and Kazakhstan in the field of Computer Science. Among this year’s winners were three HSE students, including Alexander Grishin, a Doctoral student of the Big Data and Information Retrieval School of the HSE Faculty of Computer Science. Alexander spoke to us about his work, research challenges, and why he was surprised to receive the award.
The British Educational Research Association, BERA, has announced the winners of its Early Career Researchers Award, founded this year to recognize emerging researchers. The new award’s first two winners are Saule Bekova, Research Fellow at theInstitute of Education of the Centre of Sociology of Higher Education, and Binwei Lu of Durham University (UK). In the announcement, the organizing committee praised them as ‘outstanding scholars’ and noted the potential significance of their research for the field of education.
At the 2021 RusCrypto Competition, HSE PhD student Anastasia Malashina (MIEM) won the student report category by the unanimous decision of the competition committee, which included the directors of the RusCrypto-2021 Association, members of the programme committee, and experts in the field of computer security. Anastasia spoke with HSE News Service about her presentation.
In 2010, HSE University launched the Full-time Advanced Doctoral Programme, an innovative programme for doctoral students at that time. At the beginning of 2021, an alumna of the programme’s first cohort became the first programme graduate to earn her Doctor of Sciences degree: Elena Vakulenko, Assistant Professor at HSE University’s Faculty of Economic Sciences. In an interview with HSE News Service, the professor discussed her academic career.
In Pursuit of the African Migrant Experience: HSE Doctoral Student Sheds Light on an Understudied Segment of the Moscow Population
At the recent seminar of the HSE International Laboratory for Social Integration Research, Isaac Olumayowa Oni, HSE doctoral student and lab research assistant, spoke about African migration to Russia. In an interview with HSE News Service, Isaac discussed his research and talked about what it’s like to study at HSE and live in Moscow.