University of Guelph

Vector Institute

Ontario, Canada

I am to understand the principles of deep neural network generalization and robustness to distribution shift, so that models may be deployed with relevant performance guarantees. I’m particularly interested in the application of information theory to deep learning in the service of Engineering standards and best practices.

My research interests are motivated by the “adversarial examples” phenomenon, whereby models can be fooled by seemingly unimportant input manipulations. This phenomenon poses a challenge to traditional interpretations of features extracted by deep neural networks, and limits their use in the sciences and performance-critical settings.

Toward trustworthy machine learning and artificial intelligence, I believe that more systematic ethical oversight is required to maintain the privilege of self-regulation, trust in the field, and ultimately that the public interest remains at the forefront of our research.