Daniel Roy

Associate Professor
Ontario Power Building, Room 9140, 700 University Avenue, 9th floor, Toronto, ON M5G 1X6



Vector Institute
Department of Computer and Mathematical Sciences, UTSC
Department of Computer Science, University of Toronto
Department of Electrical and Computer Engineering, University of Toronto

Fields of Study

Areas of Interest

  • Machine learning
  • Statistical learning theory
  • Foundations of statistics; nonstandard analysis
  • Online learning; bandits; and other models of adversarial learning
  • Statistical analysis of data with complex structures (graphs, arrays, etc.); probabilistic symmetries
  • Probabilistic programming
  • Bayesian nonparametric statistics


Daniel Roy is Canada CIFAR AI Chair at the Vector Institute and associate professor in the Departments of Statistical Sciences, Computer Science, Electrical and Computer Engineering, and Computer and Mathematical Sciences. Roy's research spans machine learning, mathematical statistics, and theoretical computer science. Roy is a recent recipient of a NSERC Discovery Accelerator Supplement, an Ontario Early Research Award, and a Google Faculty Research Award. Prior to joining Toronto, Roy was a Research Fellow of Emmanuel College and Newton International Fellow of the Royal Society and Royal Academy of Engineering, hosted by the University of Cambridge. Roy completed his doctorate in Computer Science at the Massachusetts Institute of Technology, where his dissertation was awarded the MIT EECS Sprowls Award, given to the top theses in computer science in that year.


PhD, Massachusetts Institute of Technology
MEng, Massachusetts Institute of Technology
BSc, Massachusetts Institute of Technology