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Web-site of my group and our research seminar (joint with the group of Elena Gryazina)

Yury Maximov

Assistant Professor
Center for Energy Systems

Yury is a graduate of the Moscow Institute of Physics and Technology (MIPT).  After obtaining the Candidate of Sciences degree in discrete mathematics, he joined INRIA Rhone –Alpes and Grenoble Alpes University, France, as a postdoctoral researcher and turned his interest to statistical machine learning and optimization. Today, his research interests and experience include discrete mathematics, huge-scale convex optimization, statistics and statistical machine learning, as well as their engineering. Throughout his career, Yury always showed a deep appreciation of fundamental knowledge and strong practical motivation. He was a Team Lead and PI of multiple industrial projects on huge-scale optimization for telecommunication and railways networks. Prior to joining Skoltech, Yuri was an Associate Professor at the Department of Computer Science at the National Research University Higher School of Economics (HSE), MIPT Department of Control and Applied Mathematics, and a Research scientist at the Institute for Information Transmission Problem RAS (IITP RAS).

In 2015, Yury formed a research group “Optimization and Statistical Learning” of talented BSc, MSc and PhD students from MIPT, HSE and IITP, who conduct the research on the border of numerical optimization, computational complexity and statistics/statistical learning with a special focus on application of the research results in networking, engineering and physics. They also do research motivated by industrial applications arising from their collaboration within IITP and PreMoLab MIPT teams with Huawei, Genplan Moscow and other companies.

Yury is also an inspiring teacher readily sharing his knowledge and encouraging his students to take upon real challenges. Yuri taught undergraduate and graduate courses in applied mathematics at MIPT and HSE, and gave a number of invited talks and lectures at Weierstrass Institute for Applied Analysis and Stochastics (Berlin), Yandex (Yekaterinburg), Baltic Federal University (Kaliningrad), Bosch (Stuttgart) among others. Since November of 2015, Yury has held weekly research seminars on Modern Huge-Scale Optimization at IITP RAS.

Optimization, learning and engineering applications: discrete and convex optimization, computational complexity, statistical learning, network design and control (including engineering problems that arise in power flow and transportation networks).



  1. Gasnikov, A., Yu. Dorn, P. Dvurechensky and Yu. Maximov. Searching Equillibriums in Beckmann’s and Nesterov–de Palma’s Models, Mathematical Models and Computer Simulations (to appear), 28(10):40–64. October 2016.
  2. Maximov, Yu., M.-R. Amini, and Z. Harchaoui.  Rademacher Complexity Bounds for a Penalized Multiclass Semi-supervised Algorithm. arXiv preprint arXiv:1607.00567 (submitted to Machine Learning Journal). 2016.
  3. Iofina, G. and Yu. Maximov. Reduction Based Similarity Learning for High Dimensional Problems. Pattern Recognition and Image Analysis, 26(2):374–378. April 2016.
  4. Maximov, Yu. and D. Reshetova. Tight Risk Bounds for Multi-Class Margin Classifiers. https://arxiv.org/abs/1507.03040. 2016.
  5. Anikin, A., A. Gasnikov, A. Gornov, D. Kamzolov, Yu. Maximov and Yu. Nesterov. Effective Numerical Methods for Huge-scale Linear Systems with Double-sparsity and Applications to PageRank. MIPT Proceedings, 7(4):74–94. 2015.
  6. Reshetova, D. and Yu. Maximov.  Multiclass One-vs-All Classifer Complexity.  MIPT Proceedings, 7(4):59–66. 2015.
  7. Podkopaev, A., M. Karasikov and Yu. Maximov. Protein Packing by Semi-definite Programming. MIPT Proceedings, 7(4):66–73. 2015.
  8. Maximov, Yu. Shortest and Minimal Disjunctive Normal Forms of Complete Functions. Computational Mathematics and Mathematical Physics, 55(7):1242–1255.
  9. Karasikov, M. and Maximov, Yu. Dimensionality reduction for multi-class learning problems reduced to multiple binary problems.  Journal of Machine Learning and Data Analysis. 1(9):1273:1290. 2014.
  10. Maximov, Yu. Implementation of Boolean Functions with a Bounded Number of Zeros by Disjunctive Normal Forms. Computational Mathematics and Mathematical Physics, 53(9):1391–1409, 2013.
  11. Maximov, Yu.   Comparative Analysis of the Complexity of Boolean Functions with a Small Number of Zeros. Doklady Mathematics, 86(3):854–856. 2012.
  12. Maximov, Yu. Simple Disjunctive Normal Forms of Boolean Functions with a Restricted Number of Zeros. Doklady Mathematics, 86(1):480–482. 2012.
  13. Maksimov, Yu. Correct Algebras Over Estimation Algorithms in the Set of Regular Recognition Problems with Nonoverlapping Classes. Computational Mathematics and Mathematical Physics, 49(7): 1264–1275. 2009.


14. Maximov Yu. and M. Mendel. “Efficient Traffic Measurements for Huge-Scale Internet Networks via Convex Optimization” European Chapter on Combinatorial Optimization (ECCO-29), Budapest, May 2016.

  1. Reshetova, D. and Maximov, Yu. “Generalization Error Bounds for Multi-Class Classi-fiers”. Information Technologies and Systems (ITAS-39), 6-11 September 2015. Best paper award
  2. Anikin, A., N. Buzun, P. Dvurechensky, A. Gagloev, A. Gasnikov, A. Golov, A. Gornov, A. Gubaydullin, Yu. Maximov, M. Mendel and V. Spokoiny. “High-Dimensional Un-determined Linear Systems: Numerical Methods and Modeling Assumptions.” Infor-mation Technologies and Systems (ITAS-39), 6-11 September 2015.
  3. Podkopaev, A. and Yu. Maximov. “Optimal Protein Packing by Convex Optimization”. Information Technologies and Systems (ITAS-39), 6-11 September 2015.
  4. Iofina, G. and Yu. Maximov. “A new Method for Learning Similarity Functions in High Dimensional Problems”. 9-th Open Russian German Workshop on Pattern Recognition and Image Understanding (OGRW-9). Koblenz, Germany, 1-5 December 2014. (Section Talk)
  5. Maximov, Yu. “Maximization of Trigonometric Polynomials over the Sphere and Approximation Bounds for Boolean Quadratic Programming Problems.” Information Technologies and Systems (ITAS-37). September 2013.
  6. Iofina, G., Yu. Maximov, A. Minaev, Yu. Polyakov. Fast Logical Predictors for Genotype-Phenotype Mapping. 20-th International Conference of Operational Research Societes (IFORS-20). Barcelona, July 2014.


21.  Maximov, Yu. “Improved polynomial time approximation guarantees for well structured quadratic optimization problem”, The 5th International Conference on Network Analysis (NET-2015), Nizhny Novgorod, May 2015.

  1. Gagloev, A., N. Buzun, Yu. Dorn, A. Gasnikov, A. Golov, A. Gubaydullin, Yu. Maximov and M. Mendel. “Sparsity and randomization techniques in huge scale traffic matrix estimation problems”, The 5th International Conference on Network Analysis (NET-2015), Nizhny Novgorod, May 2015.


23.  Maximov, Yu. Effective numerical methods for huge-scale sparse linear systems with applications to the PageRank problem. University Grenoble-Alpes, Grenoble, France. 27 July, 2016 (anticipated).

  1. Maximov, Yu. Structured Semi-Definite Programming with Applications to Non-Gaussian Component Analysis. Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany. 25 May, 2016.
  2. Maximov, Yu. Efficient Algorithms for the PageRank Problem. Russian Academy of Sciences, Nanotechnology and Information Science Department, Moscow, 8 November 2015.
  3. Maximov, Yu. Efficient Numerical Methods for Huge-Scale Problems with Structured Sparsity (invited plenary talk). Bosch CRIMSON X-Divisional Workshop on Optimization, Stuttgart, Germany. 9-10 September, 2015.


Nikita Doikov
Research Intern
Oleg Gorodnitskii
Research Intern
Valeriya Kovaleva
Research Intern
Mikhail Krechetov
Research Intern
Igor Molybog
Research Intern
Roman Pogodin
Research Intern
Andrii Riazanov
Research Intern
Sergey Volodin
Research Intern

ФИО: Максимов Юрий Владимирович

Занимаемая должность (должности): Старший Преподаватель

Ученая степень: Кандидат физико-математических наук по специальности “Дискретная математика”, 2012, Московский физико-технический институт

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Прикладные физика и математика

Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): нет

Общий стаж работы: 7 лет

Стаж работы по специальности: 7 лет