University of Guelph

Vector Institute

Ontario, Canada

I work on deep learning theory and methodology, so that models can be deployed with useful performance guarantees in practice. I’m particularly interested in the application of information theory to deep learning, and in the service of Engineering standards for its use.

My current research interests are motivated by the “adversarial examples” phenomenon, which limits opportunities for the responsible deployment of deep learning models in performance-critical settings, e.g., commercial autopilots, or medical imaging. More generally, I am interested in privacy and security, causal discovery, and embedded systems.