This programme stands at the crossroads of various disciplines of modern mathematics and computer science, including statistics, optimization, learning theory, information theory, complexity theory, as well as at the intersection of science and innovation in the field of modern information technology. Leading experts at HSE and Skoltech jointly provide instruction in this unique research-driven programme.
Students participate in one or more working groups (research seminars), where they determine focus areas for an initial survey report and then solve challenges at the intersection of cutting-edge research and technology in statistical learning theory. These seminars are built on teamwork, as the undertaken tasks are so complex that they can’t be solved by one person alone. Students learn how to effectively collaborate, bringing together their diverse collective skills, competencies, and experiences to determine successful solutions for complicated issues.
Programme courses are taught by leading HSE experts, including globally renowned scholars such as Dr. Yurii Nesterov, Dr. Denis Belomestny, Dr. Dmitry Vetrov, Dr. Andrei Sobolevski, Dr. Alexey Naumov, and Dr. Quentin Paris. Lectures are also delivered by Skoltech professors including Dr. Ivan Oseledets, Dr. Viktor Lempitsky, Dr. Evgeny Burnaev, and Dr. Yury Maximov. This team is rather young, but its members have already made significant research achievements.
The programme actively cooperates with the Russian Academy of Sciences Institute for Information Transmission Problems, as well as with relevant faculties at Moscow State University and the Moscow Institute of Physics and Technology. Graduates go on to work for major Russian and international companies and are in high demand for their exceptional mathematical skills.
The programme aims at preparing researchers in the most dynamic and high-demand fields related to mathematics and computer science. Graduates of the Master's program may pursue a practical or research-oriented career, both of which are popular in one of the following areas:
carrying out analysis in industry, consultancy, various types of associations and foundations, government agencies, banks, investment funds, etc.;
expert activities related to methodological development, probabilistic modeling, statistical estimates, transport planning, optimization and forecasting tasks, as well as coming up with efficient methods, control technologies and data analysis in a variety of professional specializations;
providing technical support for analytical and consulting groups engaged in machine learning, engineering design, financial analysis, modeling and optimization of transport networks;
participating in management teams of analytical, research and administrative departments.
Graduates of the Statistical Learning Theory Master's programme will receive sufficient instruction to continue with studies and research at leading global and Russian centres of applied mathematics, mathematical modeling and computer science, such as the Laboratory of Stochastic Algorithms and Nonparametric Statistics Institute for Weierstrass Applied Analysis and Stochastics and the Faculty of Mathematics, Humboldt University (Berlin), Catholic University of Louvain (Belgium), Joseph Fourier University (Grenoble), Max Planck Institute for Mathematics (Bonn), University of Mannheim, ENSAE ParisTech (Paris), and Steklov Mathematical Institute (Moscow). Furthermore, many leading companies, such as Yandex, Google, Microsoft, Bosch, Huawei, and Siemens, are very interested in experts with such a background.
Students are selected on the basis of their portfolio, which includes required and optional components. The Admission Committee may invite the applicant for an interview.
Bachelor’s (Specialist’s or Master’s) diploma and official transcripts of previous educational studies. (if you have not yet received your Bachelor’s diploma, please include an official copy of your most recent academic transcript).
You need to have a degree in Computer Science or Applied Mathematics and Informatics or at least to have courses in Algorithms and Data Structures, Programming, Databases Theory and Advanced Mathematics (Calculus, Linear Algebra, Probability Theory and, Mathematical Statistics) in your diploma or certificates.
The Admission Committee takes into consideration the number of hours, the final assessments, and your university ranking.
CV, which confirms your professional experience (please indicate your position and the list of duties) or scientific activity in the Applied Mathematics and Informatics field of study, including educational internship.
Letter of motivation (~500 words, not more than one page of printed text), describing your reasons for applying in the context of your long-term career goals and background. The quality of your English is also evaluated.
At least one letter of recommendation. Please provide your recommenders with HSE’s letter of recommendation guidelines.
Exam results confirming language proficiency. Valid IELTS certificate (>= 5.5), TOEFL IBT(>= 70), TOEFL PBT (>= 500).
List of articles in scientific and professional journals, conferences, collections of student work, etc., indicating the author's name and the title of the publication.
Professional development, training, specialized online courses, etc. The list of courses in which took part, with an indication of the received document / certificate.
Any additional academic evidence that may help the selection committee evaluate the candidate in the positive light, such as certificates of prior academic achievements, publications and conference presentations, participation in academic Olympiads.