University of Guelph
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.
|May 22, 2019||New work Batch Normalization is a Cause of Adversarial Vulnerability to appear in the Workshop on Identifying and Understanding Deep Learning Phenomena at ICML 2019.|
|Mar 25, 2019||Awarded a Dean’s Graduate Scholarship from the College of Engineering & Physical Sciences at the University of Guelph, and an Ontario Graduate Scholarship.|
|Mar 2, 2019||I will review for the 33rd Conference on Neural Information Processing Systems|