Dale Schuurmans

Dale Schuurmans

Fellow & Canada CIFAR AI Chair

Academic Affiliations

Professor – University of Alberta (Computing Science); Principal Investigator – Reinforcement Learning & Artificial Intelligence Lab (Computing Science)

Industry and Research Affiliations

Senior Staff Research Scientist – Google Brain

Areas of Expertise

Artificial intelligence; machine learning; reinforcement learning; deep learning; probability modelling; optimization; search

Dale Schuurmans’ long term research goal is to develop systems that learn predictive models from massive data sources when the requisite models are complex

Modeling complexity

Dale Schuurmans’ long term research goal is to develop systems that learn predictive models from massive data sources when the requisite models are complex – for example: in perception, language interpretation, information extraction, bioinformatics, or robot learning. Some of the key challenges he tackles are knowledge representation for learning -- how to usefully express and debug prior domain assumptions -- and navigating complex model spaces -- how to find good models while avoiding over/under-fitting. Some of Dale’s ongoing projects include statistical natural language modelling, reinforcement learning, and learning search control. He has also developed new methods for probabilistic inference, optimization, and constraint satisfaction. He has worked in many areas of machine learning and artificial intelligence, including model selection, on-line learning, adversarial optimization, boolean satisfiability, sequential decision making, reinforcement learning, Bayesian optimization, semi-definite methods for unsupervised learning, dimensionality reduction, and robust estimation.

Dale is a Fellow and Canada CIFAR AI Chair at Amii, a Professor in the Department of Computing Science at the University of Alberta and a Senior Staff Research Scientist at Google Brain in Edmonton, Canada. He is the Associate Editor in Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence and sits on the Advisory Board for the Neural Information Processing Systems (NeurIPS) conference. Dale is also a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and has received best paper awards at top-tier conferences such as NeurIPS, the IEEE International Conference on Automation and Logistics and at the International Conference on Machine Learning (ICML). Dale has co-authored more than 300 papers, published in venues such as the International Joint Conference on Artificial Intelligence (IJCAI), AAAI, ICML, and ICAL. Dale has supervised more than 50 early-career researchers at the M.Sc and Ph.D. levels.