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Michael Chertkov

Adjunct Professor
Material Science
Skoltech
Russian Federation

Biography

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.

Research Interest

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.

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