Kevin Leyton-Brown
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2019
Kevin Leyton-Brown is a Canada CIFAR AI Chair at Amii, and a professor of Computer Science at the University of British Columbia. He is the Director of the Centre for Artificial Intelligence Decision-Making and Action (CAIDA) at UBC’s Institute for Computing, Information and Cognitive Systems (ICICS) and also the Director of a new research cluster on AI Methods for Scientific Impact (AIM-SI) in UBC’s Faculty of Science. Leyton-Brown acts as an advisor to AI21 Labs, Auctionomics and OneChronos.
Leyton-Brown conducts research at the intersection of machine learning and both the design and operation of electronic markets and the design of heuristic algorithms. He helped to design a $20B auction that reallocated North American radio spectrum; an electronic market that allowed Ugandan farmers to find buyers for surplus crops; and widely used open source software packages such as SATzilla (an algorithm portfolio for solving satisfiability problems), Mechanical TA (peer grading software used at universities around the world), and AutoWEKA (an add-on for a widely-used machine learning framework that simultaneously performs model selection and hyperparameter optimization). He is increasingly interested in large language models, particularly as components of agent architectures. He believes we have both a moral obligation and a historical opportunity to leverage AI to benefit underserved communities, particularly in the developing world.
He has co-written two textbooks, co-taught two Coursera courses on “Game Theory” to over a million students (and counting!), and received awards for his teaching at UBC, including a Killam Teaching Prize.
Awards
- Fellow, Royal Society of Canada, 2023
- Research Track Test of Time Award, ACM SIG-KDD, 2023
- Distinguished Scholar Award, UBC, 2021
- Prominent Paper Award, Artificial Intelligence Journal, 2021
- Fellow, Association for Computing Machinery (ACM), 2020
- Fellow, Association for the Advancement of Artificial Intelligence (AAAI), 2018
- INFORMS Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science, 2018
Relevant Publications
- Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023). In-context retrieval-augmented language models. In Transactions of the Association for Computational Linguistics 11 (pp. 1316 - 1331).
- Zarkoob, H., d'Eon, G., Podina, L., & Leyton-Brown, K. (2023). Better peer grading through Bayesian inference. In Proceedings of the AAAI Conference on Artificial Intelligence 37(5) (pp. 6137 - 6144).
- Hartford, J., Lewis, G., Leyton-Brown, K., & Taddy, M. (2017. Deep IV: A flexible approach for counterfactual prediction. In International Conference on Machine Learning, pp. 1414-1423.
- K Leyton-Brown, P Milgrom, I Segal, Economics and computer science of a radio spectrum reallocation. Proceedings of the National Academy of Sciences 114 (28), 7202-7209, 201
Shoham, Y., & Leyton-Brown, K. (2008). Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press.
Xu, L., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2008). SATzilla: portfolio-based algorithm selection for SAT. Journal of artificial intelligence research, 32, 565-606.