Dr. Bailey's primary research interest is in the development of interdisciplinary approaches to understand problems related to optimization. To this end, he is creating connections between online optimization/machine learning and physics to advance the understanding of multi-agent learning systems such as generative adversarial networks. He is also working in the intersection of operations research, behavioral economics, and computer science to better understand social choice (group decision making) in areas such as voting, matching, and auctions. More generally, Dr. Bailey is interested in problems related to combinatorial optimization, game theory, machine learning, graph theory and duality theory.
