The Speakers

In this talk we discuss several approaches to the formulation of distributionally robust counterparts of Markov Decision Processes,  where the transition kernels are not specified exactly but rather are assumed to be elements of the corresponding ambiguity sets.  The intent is to clarify some connections between the game and static formulations of distributionally robust MDPs,  and explain the role of rectangularity associated with ambiguity sets in determining these connections.

Alexander Shapiro is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. In 2013 he was awarded Khachiyan Prize of INFORMS for lifetime achievements in optimization, and in 2018 he was a recipient of the Dantzig Prize awarded by the Mathematical Optimization Society and Society for Industrial and Applied Mathematics.  In 2020 he was elected to the National Academy of Engineering. In 2021 he was a recipient of John von Neumann Theory Prize awarded by INFORMS.