michaelchertkov

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Michael Chertkov

Adjunct Professor
Center for Energy Systems

Michael is working at Skoltech part-time, as a Founding Faculty Fellow initially (since 2011) and most recently as an Adjunct Professor of the Center for Energy Systems. Michael is the director of the Energy Systems Science and Engineering M.Sc. and Ph.D. program at Skoltech. When not in Moscow, Michael also works at the Los Alamos National Laboratory as a technical staff member in the Theoretical Division.

Michael’s areas of interest include statistical and mathematical physics applied to energy and communication networks, machine learning, control theory, information theory, computer science, fluid mechanics and optics. He has published more than 180 papers in these research areas. Dr. Chertkov received his Ph.D. in physics from the Weizmann Institute of Science in 1996, and his M.Sc. in physics from Novosibirsk State University in 1990. After his Ph.D., Dr. Chertkov spent three years at Princeton University as a R.H. Dicke Fellow in the Department of Physics. He joined Los Alamos National Lab in 1999, initially as a J.R. Oppenheimer Fellow in the Theoretical Division. He is now a technical staff member in the same division.

Michael is an editor of the Journal of Statistical Mechanics (JSTAT), associate editor of IEEE Transactions on Control of Network Systems, member of the Editorial Board of Scientific Reports (Nature Group), a  fellow of the American Physical Society (APS) and a senior member of IEEE.

Projects for Students:  

Prof. Chertkov is currently looking  for M.Sc. and Ph.D. students well trained in mathematics (and/or physics or statistics)   to work on the following projects (the list is brief and incomplete):

  1. Modeling and Control of Heat and Mass Flows in District Heating Systems.
  2. Modeling, Optimization and Planning of Natural Gas Systems.
  3. Modeling and Controlling Energy Resources at the Distribution Level. (Demand Response.)
  4. Development of  novel Data Driven (Machine Learning) approaches to Energy Systems
  5. Interdependency of modern  Energy Systems (Power-, Gas and Heat)

Prof  Chertkov’s research at Skoltech focuses on applied and theoretical problems in planning, optimization and operations of energy systems of Russia and the World, including power systems, natural gas systems and district heating systems.  He applies and develops various methods from statistical and mathematical physics as well as theory and algorithms from various disciplines of theoretical engineering, including but not limited to, statistical inference and graphical models, optimization and control, machine learning, information theory and computer science.

Most representative/cited/significant publications:

  • Options for control of reactive power by distributed photovoltaic generators, K Turitsyn, P Sulc, S Backhaus, M Chertkov, Proceedings of the IEEE 99 (6), 1063-1073
  • Normal and anomalous scaling of the fourth-order correlation function of a randomly advected passive scalar, M Chertkov, G Falkovich, I Kolokolov, V Lebedev, Physical Review E 52 (5), 4924 (1996)
  • Synchronization in complex oscillator networks and smart grids, F Dörfler, M Chertkov, F Bullo, Proceedings of the National Academy of Sciences 110 (6), 2005-2010 (2013)
  • Lagrangian tetrad dynamics and the phenomenology of turbulence, M Chertkov, A Pumir, BI Shraiman, Physics of fluids 11 (8), 2394-2410 (1999)
  • Statistics of a passive scalar advected by a large-scale two-dimensional velocity field: Analytic solution, M Chertkov, G Falkovich, I Kolokolov, V Lebedev, Physical Review E 51 (6), 5609 (1995)
  • Loop series for discrete statistical models on graphs, M Chertkov, VY Chernyak, Journal of Statistical Mechanics: Theory and Experiment 2006 (06), P06009 (2006)
  • Anomalous scaling exponents of a white-advected passive scalar, M Chertkov, G Falkovich, Physical review letters 76 (15), 2706 (1996)
  • Chance-constrained optimal power flow: Risk-aware network control under uncertainty, D Bienstock, M Chertkov, S Harnett, SIAM Review 56 (3), 461-495 (2014)
  • Path-integral analysis of fluctuation theorems for general Langevin processes, VY Chernyak, M Chertkov, C Jarzynski, Journal of Statistical Mechanics: Theory and Experiment 2006 (08), P08001 (2006)
  • Senior Member of IEEE (2015)
  • American Physical Society Fellow (2011)
  • R. Oppenheimer Fellowship at Los Alamos National Laboratory (1999)
  • H. Dicke Fellowship at Princeton (1996)
  • Prize of the Feinberg Graduate School (1996)
  • Prize of the Charles Clore Israel Foundation (1995)
ilialuchnikov
Ilia Luchnikov
Research Intern

1. Stochastic Modeling and Computations (Apr-May 2016, Apr-May 2017 at Skoltech)

The course offers a soft and self-contained introduction to modern applied probability, covering theory and application  of  stochastic  models.   Emphasis is  placed  on  intuitive  explanations  of  the  theoretical  concepts, such as random walks, law of large numbers, Markov processes, reversibility, sampling, etc., supplemented by practical/computational implementations of basic algorithms.  In the second part of the course, the focus shifts from general concepts and algorithms per se to their applications in science and engineering with examples, aiming to illustrate the models and make the methods of solution, originating from physics, chemistry, machine learning, control and operations research, clear and exciting.

2. Graphical Models of Statistical Inference (Sep-Oct 2016 at Skoltech)

This course is recommended for IT students, as well as other specialization students, interested in learning about modern theoretical and practical approaches to analysis of big data sets with reach statistical correlations expressed through graphs, matrices, tensors and related. The course is light on rigorous proofs, but rich on statistics and physics intuition. This mini-course consists of the following six lectures:

  1. Graphical Models (Language) and Structured Statistical Inference (problem formulations) in Computer Science, Information Theory and Physics (intro).
  2. Computational Complexity & Algorithms (Deterministic & Stochastic). Statistical Inference as an Optimization — from Partition Function and Marginal Probabilities to Free Energy (Kublack-Leibler Functional).
  3. Mean-Field, Belief Propagation, Linear Programming — Variational Approaches, Relaxations, Lower and Upper Bounds. Exact & Heuristic approaches. Iterative Algorithms.
  4. Modern Analysis and Algorithmic Tools. Review of Loop Series, Cummulant Expansions, Computational Trees, Graph Cover & Monte-Carlo Approaches.
  5. Examples of Tractable Graphical Models: (a) Network Flows; (b) Attractive (Ferromagnetic) Ising Models; (c) Matching Models; (d) Planar (det-reducable) Models; (e) Gaussian Graphical Models.
  6. Open Problems. Various Applications, e.g. in Machine Learning, Energy, Bio and Social Systems. Connections/links to other areas of research in modern theoretical engineering.

3. Introduction to Power Systems (2014 Feb-March at Skoltech)

The course provides a graduate level overview of modern power systems, with a major emphasis on the system aspects of power production, transmission, storage and delivery. All stages of the power energy technical chain will be thoroughly reviewed from a comprehensive engineering prospective but also from the standpoint of fundamental physics principles/equations. A special emphasis will be given to improving students’ ability to extract well-formulated physics and mathematics problems from power engineering reality. The course relies on strong undergraduate math/physics background of the students, however no background in power systems will be assumed or required. In this course we will advance gradually through major principles of the power system design (with some history re-course), discussion of major elements of the power systems (generators, power lines, transformers), analyze state estimation, optimization, control and design practice, and will conclude with discussions of modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/resilient grid technologies.

 

ФИО: Чертков Михаил Викторович

Занимаемая должность (должности): Профессор

Преподаваемые дисциплины: Введение в энергетические системы

Ученая степень: PhD, 1996 г., физика, Институт Вейцмана, Израиль; кандидат физико-математических наук, 1990, Новосибирский государственный университет

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Физика

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

Общий стаж работы: 19 лет

Стаж работы по специальности: 19 лет