Invited Speakers

Bio: Jeff Decary is a Ph.D. student in the School of Business at the University of Connecticut. Before starting his Ph.D., he received his M.Sc.A. degree in Applied Mathematics from Polytechnique Montréal and his B.Sc. in Actuarial Science from UQAM. Throughout his graduate studies, his work has primarily focused on integrating machine learning and optimization to solve stochastic optimization problems in the context of sports betting.

Bio: Pascal Poupart is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Waterloo (Canada). He is also a Canada CIFAR AI Chair at the Vector Institute and a member of the Waterloo AI Institute. He serves on the advisory board of the NSF AI Institute For Advances in Optimization (2022-present) at Georgia Tech and Berkeley. He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab at the Royal Bank of Canada (2018-2020). He also served as scientific advisor for ProNavigator (2017-2019), ElementAI (2017-2018) and DialPad (2017-2018). He received the Ph.D. in Computer Science at the University of Toronto (2005). His research focuses on the development of algorithms for Machine Learning with application to Natural Language Processing and Material Design. He is most well-known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team are currently working on include object-oriented, risk-sensitive and constrained reinforcement learning, Bayesian federated learning, probabilistic deep learning, conversational agents, automated document editing, sport analytics, adaptive satisfiability and material design for CO2 conversion & capture.  Pascal Poupart received a Canada CIFAR AI Chair at the Vector Institute (2018-present), outstanding performance awards at the University of Waterloo (2017, 2021), a Cheriton Faculty Fellowship (2015-2018), a best student paper honourable mention (SAT-2017), a silver medal at the SAT-2017 competition, and a gold medal at the SAT-2016 competition. 

Bio: Oliver Schulte is a Professor in the School of Computing Science at Simon Fraser University, Vancouver, Canada. He received his Ph.D. from Carnegie Mellon University in 1997. His current research focuses on machine learning for structured and event data. He has published sports analytics papers in leading AI and machine learning venues, and co-organized two hockey analytics conferences. He spent two years working with Sportlogiq, a leading hockey data provider. While he has won some nice awards, his biggest claim to fame may be a draw against chess world champion Gary Kasparov.   

Bio: Michael Trick is Dean of Carnegie Mellon University in Qatar and is the Harry B. and James H. Higgins Professor of Operations Research at CMU's Tepper School of Business.  His research is primarily in the area of computational methods for integer programs and the practical application of those approaches.  He has consulted extensively for many sports leagues on scheduling issues, and with a variety of companies and government agencies on scheduling and resource allocation.  He was part of the Federal Communications Commission team that received the Edelman Prize for implementing an auction approach for frequency assignment. He is a Fellow of INFORMS and the International Federation of Operational Research Societies. 


Bio: Rachael Walker recently graduated from the University of Toronto with a BASc in Industrial Engineering and a minor in Artificial Intelligence Engineering. For her undergraduate thesis, she researched the application of stochastic optimization models in sports. Specifically, she examined how Markov models can be used to design fair handicap systems in the game of darts. Rachael has just started a new role on the data science team at the private equity firm Birch Hill Equity Partners. 


Bio: Tauhid is an Associate Professor of Operations Management at the Yale School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT.  His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and AI.  Some of the topics he studies in the social networks space include combating online extremists and assessing the impact of bots.  His broader interests cover data driven approaches to investing in startup companies, algorithmic sports betting, and biometric data.  His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, and Time Magazine.