Colloquium: Mean Field Theory with Nicolas Gast

Wednesday, April 25, 2018 - 12:00 to 14:00
Room 2311, Old CS Building

Title: How to use mean field approximation for small systems

Abstract: Mean field approximation is a widely used technique to study stochastic systems composed of many interacting objects with applications from theoretical physics to biological models and artificial intelligence. In computer science, mean field approximation has been successfully used to analyze the performance of many distributed algorithms, including allocation strategies in server farms, caching algorithms and wireless protocols. The fundamental idea of mean field approximation is the behavior of a large stochastic system os often simpler than the one of a moderate size system because of the law of large number. In this talk, I will introduce the key concepts behind mean field approximation, by giving some examples of where it has been applied. I will review some of the classical models. I will try to answer a very natural question: how large should the system be for mean-field to apply? This leads to a follow-up question: how to refine this approximation to make it applicable for small to moderate size systems?

Bio: Nicolas Gast is a tenured research scientist at Inria (Grenoble, France) since 2014. He graduated from Ecole Normale Superieure (Paris, France) in 2007 and received a Ph.D. from the University of Grenoble in 2010. He was a research fellow at EPFL from 2010 to 2014. His research focuses on the development and the use of stochastic models and optimization methods for the design of distributed control algorithms in large-scale systems, with applications to communication networks, computing infrastructures and more recently energy networks (smart grids).
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Colloquium: Mean Field Theory with Nicolas Gast