I am an Assistant Professor of Computer Science at the University of Toronto, and a founding member of the Vector Institute.

My group’s research focuses on machine learning, especially deep learning and Bayesian modeling. We aim to develop architectures and algorithms that train faster, generalize better, give calibrated uncertainty, and uncover the structure underlying a problem. We’re especially interested in scalable and flexible uncertainty models, so that intelligent agents can explore effectively and make robust decisions at test time. Towards these objectives, we also aim to automate the configuration of ML systems, from tuning of optimization and regularization hyperparameters to the design of models, architectures, and algorithms. Finally, we are starting to investigate the important and neglected problem of ensuring that AI systems remain aligned with human values.

Previously, I received my BS in symbolic systems from Stanford in 2008, my MS in computer science from Stanford in 2009, and my PhD in computer science from MIT in 2014, studying under Bill Freeman and Josh Tenenbaum. From 2014-2016, I was a postdoc at the University of Toronto, working with Ruslan Salakhutdinov. Along with Colorado Reed, I created Metacademy, a web site which uses a dependency graph of concepts to create personalized learning plans for machine learning and related fields.


Department of Computer Science
University of Toronto
Office: Pratt 290F
6 King’s College Rd.
Toronto, ON M5S 3G4, Canada
Phone: 416-978-7391
e-mail: rgrosse_at_cs_toronto_edu