The Yandex School of Data Analysis teaches rare knowledge that is almost unavailable at traditional higher education institutions: new branches of mathematics and statistics, machine learning, big data processing, distributed computing, computer linguistics, bioinformatics. Its staff includes luminaries of Russian science and distinguished teachers who have worked for many years in leading Russian universities and scientific institutions, as well as Yandex’s own young specialists who have gained experience on the front lines of knowledge-intensive and complex tasks.
Deputy director of the school’s computer science department. At Yandex, Maxim heads development of the YT platform for distributed computing and data processing. Candidate of Sciences (PhD) in Mathematical Logic, Algebra and Number Theory, Associate Professor of Algorithms and Programming Technology at the Moscow Institute of Physics and Technology, department head of the Yandex computer science faculty at the National Research University Higher School of Economics. For many years he has participated in organising and conducting competitions and Olympiads in informatics for secondary and tertiary students. Adept at giving simple and understandable explanations of the most complex subject matter.
Candidate of Technical Sciences (PhD), Associate Professor of the Department of Information Security at the Bauman Moscow State Technical University, Team Lead of the Web-Content Analysis Group at Yandex. Works in computational linguistics and intelligent text processing; currently working on high-performance algorithms for determining duplicate documents. At the School of Data Analysis he teaches a course in Information Retrieval, devoted to the architecture and mechanisms of industrial information retrieval systems. Alexander was among the school’s first intake of students, graduating with distinction from the computer science department in 2009.
Candidate of Physical and Mathematical Sciences (PhD), head of Data Analysis and Predictive Modeling Lab at the Institute for Information Transmission Problems, Russian Academy of Sciences. Associate Professor of the Department of Information Transmission Problems and Data Analysis at the Moscow Institute of Physics and Technology. Likes Gaussian process regression, prediction sets, the Bernstein-von Mises theorem, change-point detection and optimal stopping rules, wavelet transformation and many other topics. Besides theoretical research, he works on solving applied engineering problems using statistical methods and data analysis – for example, in commercial aviation and the automotive industry.
Doctor of Physico-Mathematical Sciences, Professor of the Department of Mathematical Logic and Theory of Algorithms at Moscow State University’s Faculty of Mechanics and Mathematics, Professor of the Computer Science Faculty at the National Research University Higher School of Economics. Works with information theory, Kolmogorov complexity, computational complexity. Member of the European Association of Theoretical Computer Sciences and president of its Russian chapter. Member of Academia Europaea. Lectures and leads seminars for the School of Data Analysis’s Computational Complexity and Information Theory courses.
PhD, associate professor at the Skolkovo Institute of Science and Technology (Skoltech) and the Higher School of Economics’ Faculty of Computer Science. Interested in the development of new probabilistic models of data processing, especially with latent variables. At the School of Data Analysis, he teaches two courses of his own design, on Bayesian machine learning methods and on probabilistic graphical models – two areas of knowledge that greatly simplify reading of recent scientific papers from top conferences. Prefers lectures in the format of dialogue and exchange of ideas with students. Heads a Bayesian methods research group.
Doctor of Physico-Mathematical Sciences, senior researcher at the Russian Academy of Sciences’ Computing Center, Professor of Intelligent Systems at the Moscow Institute of Physics and Technology’s Department of Control/Management and Applied Mathematics, Associate Professor in the Department of Mathematical Methods of Forecasting at Moscow State University’s Faculty of Computational Mathematics and Cybernetics. A creator and administrator of MachineLearning.ru. His research group develops mathematical methods and technologies for thematic search in large bodies of text, diagnosis of diseases by electrocardiogram, and other pertinent tasks of data analysis. Has devoted his scientific career to fighting for the complete and final elimination of the rift between theory and practice. Considers data analysis to be the profession of the future, and that study of it is just as exciting as participating in solving “live” tasks.
Received his PhD in Mathematics and Statistics from the University of Melbourne, Australia, and the Grenoble Institute of Technology, a Leading French School in Engineering. Assitant Professor in Data Analysis at the Computer Science at HSE since 2014, where he reads lectures in Probability Theory and Mathematical Statistics, Methods in Data Analysis, Statistics and Experimental Design. At Shad, he reads a course on Machine Learning Algorithms. His research interests include on-line estimation, non-parametric statistics, and multifractal analysis.
Candidate of Physico-Mathematical Sciences (PhD), Associate Professor in the Department of Higher Algebra at Moscow State University’s Faculty of Mechanics and Mathematics and the Faculty of Computer Science at the National Research University Higher School of Economics. Head of Yandex’s morphology group. At the School of Data Analysis he teaches a course on natural language processing; previously he taught programming in C++. Has published about 20 research papers in computer algebra and symbolic computation.
Candidate of Philological Sciences (PhD), Associate Professor in the National Research University Higher School of Economics’ School of Linguistics, head of the Vinogradov Russian Language Institute’s Department of Theoretical Semantics. One of the authors of the New Explanatory Dictionary of Synonyms and the Active Dictionary of the Russian Language, he has published more than 100 works on theoretical and computational linguistics. He is engaged in the popularisation of linguistics, participating in the organisation of summer linguistics schools, HSE Russian-language Olympiads, Moscow Traditional Olympiads in Linguistics and other competitions. At the School of Data Analysis he teaches the Introduction to Linguistics course and enjoys involving students in linguistic work – from solving Olympiad problems and participating in surveys to joint scientific research, from which several projects have grown.
Candidate of Philological Sciences (PhD), Professor in the Chair of Computational Linguistics at the Russian State University for the Humanities, Associate Professor in the Chair of Information Transmission Problems and Data Analysis at the Moscow Institute of Physics and Technology. Head of the computational linguistics laboratory of the Russian Academy of Sciences’ Institute for Information Transmission Problems (Kharkevich Institute). Actively involved in scientific research for 45 years. Participated in the development of the ETAP-3 multipurpose linguistic processor with machine translation system. Has given lectures on theoretical and computational linguistics at Moscow State University, Munich University, Charles University in Prague, and the Autonomous University of Barcelona. Regularly speaks at the Summer School of Linguistics. At the School of Data Analysis, he teaches a course on rule-based machine translation, which is based on linguistics to a much greater extent than statistical machine translation.
Candidate of Physical and Mathematical Sciences (PhD), Associate Professor of Moscow State University’s Faculty of Computational Mathematics and Cybernetics and head of its Graphics and Media Lab. Academic program supervisor of the Applied Mathematics and Information Science bachelor degree course in the National Research University Higher School of Economics’ Computer Science Faculty. Works with computer vision, in particular video analysis – the recognition of people on video and analysis of their behaviour. Participates in organising the annual GraphiCon conference, leads a computer graphics course for the Lomonosov conference, regularly gives presentations for the Computer Science Club, and participates in organising Microsoft’s summer schools. At the School of Data Analysis he lectures on the analysis of images and videos, covering the history of computer vision as well as the latest developments in the field. Every lecture is supplemented by lab work providing a practical opportunity to try the methods that have been studied.
Has been teaching at the School of Data Analysis since 2008; organises and conducts seminars on algorithms and data structures. Introduced an automatic testing system for evaluation of programming assignments at the school. Previously headed Yandex’s ad quality group, the Yandex.Traffic infrastructure development group, and worked as an engineer in Google’s Moscow office. Currently he is the Chief Data Scientist at Yandex Data Factory, a role in which he is responsible for selection of projects and all analytical work associated with them. Organises courses on programming and project work in the National Research University Higher School of Economics’ Computer Science Faculty. A competitive programmer, he is a two-time medallist in the finals of the international ACM ICPC championship as a member of the Moscow State University team, and has trained teams for MSU, the Moscow Institute of Physics and Technology and other Moscow higher education institutions.
Senior lecturer at Skolkovo Institute of Science and Technology; defended his PhD dissertation on applied mathematics at Moscow State University. Has worked as a researcher at Yandex, Oxford University and Microsoft Research Cambridge. At Skoltech he heads the Computer Vision Group, working on image recognition problems and the analysis of biomedical images. At the School of Data Analysis he acquaints students with algorithmic methods of deep learning and the learning of representations, as well as applications of these methods in image recognition, computer vision, natural language text analysis and other fields.
Doctor of Science in biology, PhD in cybernetics, Chief Scientist at Institute of Control Sciences of the Russian Academy of Sciences. Supervises diploma projects of students of the Moscow State Institute of Radio Engineering, Electronics and Automation, Moscow Institute for Physics and Technology. Is engaged in the analysis of biological and medical data, morbidity and mortality risk estimation methodology, and the study of aging processes. At the School of Data Analysis he teaches a course on dependencies reconstruction using different types of data. The main aim of the course is background and motivation of different approaches, results interpretation, but not the study of fundamental results of mathematical statistics.
Doctor of Science (Engineering), chief researcher at the Institute of Control Problems at the Russian Academy of Sciences. In 1963 he began to study gradient methods for the minimisation of functionals and solutions of inequalities, and since then has changed topics several times: methods of stochastic approximation, algorithms of robust estimation, robust stability, the theory of linear control systems. All his life he has maintained a love for optimisation methods, and teaches a course on the subject at the School of Data Analysis. For many years he has headed the organising committee for the Control, Information and Optimization School for young researchers, held outside Moscow every summer.
A teacher at the School of Data Analysis for four years, Ivan leads seminars on the analysis of internet data, and also organises project work in the National Research University Higher School of Economics’ Faculty of Computer Science. At Yandex he heads a group developing the YT platform for distributed computing and data processing. Having started out in machine translation and natural language text processing, he has brought to Yandex a number of School of Data Analysis graduates interested in computational linguistics. Currently working with big data.
D.Sci. (mathematics), head of the applied mathematics lab at the Institute of Mathematical Problems of Biology (Russian Academy of Sciences), and head of the Department of Algorithms and Programming Technologies, School of Innovation and High Technology, Moscow Institute of Physics and Technology. Professor of the Yandex basic department in the School of Computer Science at the National Research University Higher School of Economics. Works on algorithmic problems of bioinformatics, comparative analysis of biological sequences, and also teaches informatics and mathematics to students of different age groups. Has been teaching since he was an elementary school pupil: in third grade, on his own initiative, he started tutoring his classmates. At the School of Data Analysis he teaches a course devoted to the analysis of symbolic sequences. While helping to systematise the knowledge students already possess, at the same time the course introduces a series of algorithms that are not taught in other courses at the school. Director of the Pushchino Winter School, he also regularly speaks at mathematics summer schools. Deputy chairman of the federal subject commission of the Unified State Exam on computer science; founder and chief editor of the site ege-go.ru for students and teachers of computer science.
Doctor of Physico-Mathematical Sciences; Professor in the Department of Mathematical Statistics and Random Processes, Faculty of Mechanics and Mathematics, Moscow State University. Head of the undergraduate data analysis department, Faculty of Innovation and High Technology, Moscow Institute of Physics and Technology, where he is also head of the Department of Discrete Mathematics. Professor of the joint undergraduate programs of the New Economic School and the National Research University Higher School of Economics. Author of more than 100 publications, including 20 monographs and books. Founded the annual Combinatorics and Algorithms summer school for high school students. At the School of Data Analysis, he lectures on discrete analysis and probability theory, as well as on web graphs.
Candidate of Technical Sciences, senior researcher at the Russian Academy of Sciences’ Institute for Information Transmission Problems. Works on the development of distributed computing systems and their practical application in different spheres – from high-performance scientific computing to big data analysis. Participated in a series of research projects in grid technology, volunteer computing and service-oriented scientific environments. Currently working on the Everest cloud platform, which enables the creation of computing services and the automation of calculations on distributed resources. At the School of Data Analysis he teaches the Parallel and Distributed Computing course, focusing on the development of practical skills and familiarity with the latest approaches and technologies. For example, in 2008 the MapReduce model and Hadoop technology were introduced into the course; now they are widely used in big data work.
Member of the Russian Academy of Sciences, Distinguished Professor of Moscow State University, head of the Department of Probability Theory in Moscow State University’s Faculty of Mechanics and Mathematics. Senior researcher at the Steklov Mathematical Institute. Works on a broad range of questions of general probability theory and mathematical statistics. Author of 10 monographs, eight textbooks and more than 280 scientific articles. Educated 57 Candidates and 30 Doctors of Science. A student of Andrey Kolmogorov, he is the editor-compiler of two volumes of Selected Works and other jubilee publications dedicated to the anniversary of the outstanding mathematician. In his lectures at the School of Data Analysis, he shows students how the theory of stochastic processes “works” in the field of data analysis.
Senior research scientist at Google UK. Started programming on an old-fashioned Acorn Electron, switched to philology in his student years, then returned to computer science and computational linguistics as a postgraduate. Graduated with distinction from Oxford University, and defended his doctoral dissertation at Edinburgh University. For five years he worked for Google Translate, applying randomised algorithms for language modelling. He also developed language processing methods that are dramatically different syntactically and morphologically. Later he switched to natural language processing for Google Search. At the School of Data Analysis he teaches a course on statistical machine translation. In his free time he plays folk music on violin and classical music on piano.