Kevin Leyton-Brown

Kevin Leyton-Brown is interested in the intersection of artificial intelligence, optimization and strategic behaviour.

Market games

Kevin Leyton-Brown is interested in the intersection of artificial intelligence, optimization and strategic behaviour. He conducts research in the areas of computational game theory; market design, analysis and clearing; and machine learning for optimization. His work in game theory develops computational techniques for replacing pen-and-paper methods for analyzing strategic behaviour, and he also develops general techniques for designing algorithms that are fast in practice on provably hard optimization problems. Key contributions in these areas include the first representation language for describing large, general settings in which all players interact, algorithms for efficiently answering game-theoretic questions, and novel methods for predicting human behaviour in strategic situations. He was also a leader in using machine learning methods to characterize algorithm performance, producing the world’s fastest satisfiability solvers and clearing algorithms that have had a wide impact in electronic commerce companies and government.

Kevin is a Professor of Computer Science at the University of British Columbia, a Canada CIFAR AI Chair at Amii and an associate member of the Vancouver School of Economics. He is the Director of the Centre for Artificial Intelligence Decision-Making and Action at UBC’s Institute for Computing, Information and Cognitive Systems. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and a Distinguished Member of the Association of Computing Machinery (ACM). Kevin acts as an advisor to AI21 Labs and Auctionomics and has co-founded two companies: Kudu.ug and Meta-Algorithmic Technologies. He is past chair of the ACM Special Interest Group on Electronic Commerce (SIG-Ecom), an associate editor of ACM Transactions on Economics and Computation (ACM-TEAC) and AI Access, and an Advisory Board member for the Journal of AI Research (JAIR)