CCB Special Lecture Series with Michael Chertkov

Date & Time

Machine learning and artificial intelligence play an increasingly critical role in modern science. This lecture series will provide an introductory tutorial on these methods for researchers interested in a range of physical problems, and will give participants a basic understanding of the principles of current physics-informed machine learning approaches and introduce several widely-used techniques. Topics will include model reduction, deep learning approaches to differential equations, graphical model tools, and optimization methods.

Lecture Schedule

December 14, 2020Application Agnostic Learning from Data: Questions, Formulations, Approaches
December 15, 2020Setting Up Physics (Science) Informed (Explainable) Learning: Questions, Formulations
December 16, 2020Physics Informed Model Reduction (e.g. testing, resolving or excluding models/theories)
January 5, 2021Graphical Model Tools (i.e. formulations, methods, algorithms) for Inference, Learning, and Optimization (will allow to account most naturally physics constraints, relations, dependencies, etc)
January 6, 2021Attempt of Synthesis

About the Speaker

Dr. Michael (Misha) Chertkov is a Professor of Mathematics and Chair of the Graduate Interdisciplinary Program (GIDP) in Applied Mathematics at the University of Arizona (UArizona). He also has courtesy appointments at the Department of Computer Science and GIDP in Statistics and Data Science of the UArizona. Dr. Chertkov area of focus is mathematics, including statistics and data science, applied to physical, engineered, and other systems and networks. Dr. Chertkov received his Ph.D. in physics from the Weizmann Institute of Science in 1996, spent three years at Princeton University as a R.H. Dicke Fellow in the Department of Physics, and joined Los Alamos NL in 1999, initially as a J.R. Oppenheimer Fellow and then as a Technical Staff Member. During his 20 years at LANL he led multiple LDRD/DR, DTRA, and DOE/EERE projects, in particular on “physics of algorithms”, “optimization, inference, and learning of energy systems” and “machine learning for turbulence”. Dr. Chertkov has moved to Tucson in 2019.  He has published more than 200 papers, is a fellow of the American Physical Society, and a senior member of IEEE.

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