Gasnikov Alexander Vladimirovich
Doctor of Computer Sciences (Habilitation), Professor
Head of the Laboratory of Mathematical Methods of Optimization, Head of the Department of Mathematical Foundations of Control
Education
2006 — Moscow Institute of Physics and Technology, Department of Control and Applied Mathematics, Moscow, Russia
2007 — Candidate of Computer Sciences (PhD) in Partial Differential Equations, Thesis: “Asymptotic in time behavior of solution of Cauchy problem for conservation law with nonlinear divergent viscosity”, supervisor prof. A.A. Shananin, Moscow Institute of Physics and Technology, Moscow, Russia
2011 — Associate Professor at the Department of Mathematical Foundations of Control
2016 — Doctor of Computer Sciences (Habilitation) in Mathematical Modelling and Numerical methods of Convex Optimization, Doctoral Thesis: “Searching equilibriums in large transport networks”, supervisors prof. A.A. Shananin and prof. Yu.E. Nesterov
Work Experience
since August 2024 — Member of the Council for Science and Education under the President of Russia
since August 2024 — Scientific Director of the Museum Space on the territory of the Sirius Concert Center
since 2024 — leading researcher at the Department of Mathematical Foundations of Artificial Intelligence MIAN RAS
since February 2024 — member of the Scientific Council of the AI Alliance Russia
since November 2023 — Rector of the Innopolis University
since August 2023 — Head of the Laboratory of Multi-scale Neurodynamics for Smart Systems of Skoltech
since 2023 — Chief Researcher of Skoltech
since 2023 — Head of Laboratory No. 10 of the IPPI RAS of Mathematical Foundations of Machine Learning
since 2023 — Member of the Editorial Board of the Siberian Journal of Industrial Mathematics
since 2023 — member of the Editorial Board of the Journal of Computational Mathematics and Mathematical Physics
since 2022 — Member of the Editorial board of the Journal of Optimization Theory and Applications — Q1 (one of the world's leading journals on numerical optimization methods)
since 2022 — Academic head of the PMF direction at the School of FPMI
Since 2022 — Head of the Laboratory of Mathematical Optimization Methods, Moscow Institute of Physics and Technology
since 2022 — Head of Department of Control and Applied Mathematics, Moscow Institute of Physics and Technology
since 2021 — Head of the group in the AI Center of the Institute of System Programming of the Russian Academy of Sciences
since 2020 — Professor, Moscow Institute of Physics and Technology (State University)
since 2020 — Professor, Faculty of Computer Science, Higher School of Economics
since 2019 — Head of the group at Huawei project. Russia, Moscow
since 2016 — Member of the Editorial Board of the Siberian Journal of Computational Mathematics
Since 2015 — Lead researcher at IITP RAS (Institute for Information Transmission Problems of Russian Academy of Science), Moscow, Russia
2015-2020 — Professor at the HSE (High School Economics), Computer Science Department, Moscow, Russia
2011-2020 — Professor at MIPT (Moscow Institute of Physics and Technology), Department of Control and Applied Mathematics
Research Interests
Mathematical Modeling of Traffic Flows
Optimization (Huge-Scale, Distributed and Parallel, Stochastic, Online)
Learning from optimization point of view
Research Projects and Grants
RFBR 18-29-03071 "Big Data solutions for modeling, analysis and optimization of transport processes" (2018-2021) — performer
RFBR 18-31-20005 "Development of general principles for the construction and analysis of the convergence rate of numerical optimization methods based on the concept of the objective function model" (2018-2020) — Head
RFBR 19-31-51001 Scientific Mentoring "Distributed and parallel algorithms for solving data analysis problems" (2019-2021) — Head
RFBR 19-31-90062 Graduate students "A unified view on randomized numerical methods for solving convex optimization problems" (2019-2021) — Supervisor
RFBR 19-31-90170 Postgraduate students "Search for equilibria in transport networks using direct-dual accelerated methods with auxiliary one—dimensional optimization" (2019-2021) — Head
RNF 17-11-01027 Algorithmic optimization for problems with a large number of variables (2017-2019) — participant
RNF 18-71-10108 Optimal Transport: Numerical Methods and Applications to Data Analysis (2018-2021) — Participant
State Task No. 075-00337-20-03 "Development of effective algorithms for solving large—size optimization problems" (2020-2023) - performer
RFBR No. 19-31-51001 Scientific mentoring "Distributed and parallel algorithms for solving data analysis problems" (2020-2021) — Head
RNF 21-71-30005 Development of numerical optimization methods in applications to control problems, inverse problems and training (2021-2024) — main performer
RPF 23-11-00229 Development of efficient distributed algorithms for solving optimization problems (2023-2025)— head
Awards and Recognitions
Winner of the Yahoo Award for 2019
Winner of the Ilya Segalovich Award (Yandex) for 2020
Winner of the Moscow Government Prize for 2020
Winner of the Talent Funding Award by the Institute of Strategic Research (China) for 2023
Defended dissertations
Vasily Novitsky, "New Bounds for One-point Stochastic Gradient-free Methods" (11/7/2024). Candidate of Physical and Mathematical Sciences
Alkousa Mohammad, "Numerical Methods for Non-Smooth Convex Optimization Problems with Functional Constraints" (11.06.2020). Candidate of Physical and Mathematical Sciences.
Tyurin Alexander Igorevich, "Development of a method for solving structural optimization problems" (19.11.2020). PhD Candidate in Computer Science.
Dvurechensky Pavel Evgenievich, "Numerical methods in large-scale optimization: inexact oracle and primal-dual analysis" (28.12.2020). Doctor of Computer Science.
Kamzolov Dmitry Igorevich, "Acceleration of Tensor Methods and Their Optimal Combination" (29.12.2020). Candidate of Physical and Mathematical Sciences.
Gorbunov Eduard Alexandrovich, "Distributed and Stochastic Optimization Methods with Gradient Compression and Local Steps" (23.12.2021). Candidate of Physical and Mathematical Sciences.
Omelchenko Sergey Sergeevich, "Численные методы решения задач выпуклой оптимизации больших размеров, имеющих специальную структуру" (23.12.2021). Candidate of Physical and Mathematical Sciences.
Kotlyarova Ekaterina Vladimirovna, "Поиск равновесия в многостадийных транспортных моделях"(15.12.2022). Candidate of Physical and Mathematical Sciences.
Matyukhin Vladislav Vyacheslavovich, «Ускоренный метаалгоритм и его приложения» (22.12.2022). Candidate of Physical and Mathematical Sciences.
Dorn Yuri Vladimirovich, «Модель Нестерова-де Пальмы и ее применение в задачах макроскопического моделирования транспортных потоков» (22.12.2022). Candidate of Technical Sciences.
Makarenko Dmitry Vladimirovich, «Разработка численных методов решения задач оптимизации при ослабленных условиях гладкости» (22.12.2022). Candidate of Physical and Mathematical Sciences.
Titov Alexander Alexandrovich, «Методы оптимизации для негладких задач в пространствах больших размерностей» (27.06.2023). PhD Candidate in Computer Science.
Beznosikov Alexander Nikolaevich, «Gradient-Free Methods for Saddle-Point Problems and Beyond» (30.08.2023). Candidate of Physical and Mathematical Sciences.
Rogozin Alexander Viktorovich, «Decentralized optimization over time-varying networks» (30.08.2023). Candidate of Physical and Mathematical Sciences.
Ostroukhov Petr Alekseevich, «High-order methods for optimization problems with specific structure» (28.12.2023). Candidate of Physical and Mathematical Sciences.
Публикации
2020
Dvurechensky, P., Gasnikov, A., Omelchenko, S., Tiurin, A. A Stable Alternative to Sinkhorn’s Algorithm for Regularized Optimal Transport (2020) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12095 LNCS, pp. 406-423. Scopus DOI Q3
Stonyakin, F.S., Stepanov, A.N., Gasnikov, A.V., Titov, A.A. Mirror descent for constrained optimization problems with large subgradient values of functional constraints [Метод зеркального спуска для условных задач оптимизации с большими значениями норм субградиентов функциональных ограничений] (2020) Computer Research and Modeling, 12 (2), pp. 301-317. Scopus DOI Q4
2019
Yakovlev, P.A., Anikin, A.S., Bol’shakova, O.A., Gasnikov, A.V., Gornov, A.Y., Ermak, T.V., Makarenko, D.V., Morozov, V.P., Neterebskii, B.O. Local Algorithms for Minimizing the Force Field for 3D Representation of Macromolecules (2019) Computational Mathematics and Mathematical Physics, 59 (12), pp. 1994-2008. Scopus DOI Q2
Baimurzina, D.R., Gasnikov, A.V., Gasnikova, E.V., Dvurechensky, P.E., Ershov, E.I., Kubentaeva, M.B., Lagunovskaya, A.A. Universal Method of Searching for Equilibria and Stochastic Equilibria in Transportation Networks (2019) Computational Mathematics and Mathematical Physics, 59 (1), pp. 19-33. Scopus DOI Q2
Stonyakin, F.S., Dvinskikh, D., Dvurechensky, P., Kroshnin, A., Kuznetsova, O., Agafonov, A., Gasnikov, A., Tyurin, A., Uribe, C.A., Pasechnyuk, D., Artamonov, S. Gradient methods for problems with inexact model of the objective (2019) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11548 LNCS, pp. 97-114. Scopus DOI Q3
Vorontsova, E.A., Gasnikov, A.V., Ivanova, A.S., Nurminsky, E.A. The Walrasian Equilibrium and Centralized Distributed Optimization in Terms of Modern Convex Optimization Methods by an Example of the Resource Allocation Problem (2019) Numerical Analysis and Applications, 12 (4), pp. 338-358. Scopus DOI Q4
Gasnikov, A., Dvurechensky, P., Gorbunov, E., Vorontsova, E., Selikhanovych, D., Uribe, C.A., Jiang, B., Wang, H., Zhang, S., Bubeck, S., Jiang, Q., Lee, Y.T., Li, Y., Sidford, A. Near Optimal Methods for Minimizing Convex Functions with Lipschitz p-th Derivatives (2019) Proceedings of Machine Learning Research, 99, pp. 1392-1393. Scopus
2018
Gasnikov, A.V., Gorbunov, E.A., Kovalev, D.A., Mohammed, A.A.M., Chernousova, E.O. The global rate of convergence for optimal tensor methods in smooth convex optimization [Обоснование гипотезы об оптимальных оценках скорости сходимости численных методов выпуклой оптимизации высоких порядков] (2018) Computer Research and Modeling, 10 (6), pp. 737-753. Scopus DOI Q4
Gasnikov, A.V., Dvurechensky, P.E., Zhukovskii, M.E., Kim, S.V., Plaunov, S.S., Smirnov, D.A., Noskov, F.A. About the Power Law of the PageRank Vector Component Distribution. Part 2. The Buckley–Osthus Model, Verification of the Power Law for This Model, and Setup of Real Search Engines (2018) Numerical Analysis and Applications, 11 (1), pp. 16-32. Scopus DOI Q4
2017
2016
Chernov, A., Dvurechensky, P., Gasnikov, A. Fast primal-dual gradient method for strongly convex minimization problems with linear constraints (2016) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9869 LNCS, pp. 391-403. Scopus DOI Q3