Daniel M. Roy

Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics.

• Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow).

His research explores information theories of learning, online learning, and nonstandard foundations for decision theory. Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures.

Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.