This thrust focuses on decision making under uncertainty, both in centralized and decentralized settings, and for single and multi-agent environments. A key objective is to merge ideas from stochastic programming and reinforcement learning for solving multi-stage optimization problems under uncertainty. In this thrust, the U.S. National Science Foundation AI Research Institute for Advances in Optimization explores the forecasting of highly dimensional time series, uncertainty quantification, Bayesian optimization, and decentralization learning and optimization.
AI for Scalable Online Decision-Making Team
Pieter Abbeel
University of California, Berkeley
Justin Romberg
Deputy Director
Georgia Institute of Technology
Jacob Abernathy
Georgia Institute of Technology
Alper Atamturk
Berkeley Site Director
University of California, Berkeley
Paul Grigas
University of California, Berkeley
Sven Koenig
University of Southern California
George Lan
Georgia Institute of Technology
Siva Theja Maguluri
Georgia Institute of Technology
Renato Monteiro
Georgia Institute of Technology
George Nemhauser
Georgia Institute of Technology
Arkadi Nemirovski
Georgia Institute of Technology
Haesun Park
Georgia Institute of Technology
Nick Sahinidis
Georgia Institute of Technology
Nicoleta Serban
Georgia Institute of Technology
Alexander Shapiro
Georgia Institute of Technology
Satish Thittamaranahalli
University of Southern California
Pascal Van Hentenryck
Director
Georgia Institute of Technology
Yao Xie
Georgia Institute of Technology
Tuo Zhao
Georgia Institute of Technology
Enlu Zhou
Georgia Institute of Technology
