Blair Bilodeau

Background

About Blair Bilodeau

I am an expert in decision making under uncertainty, with applications to both statistical methodology and machine learning algorithms. My work provides principled, data-based decisions that balance the need for robustness in high-stakes settings with the need for strong performance in practice. During my PhD, I developed new statistical theory and decision-making algorithms equipped with provable guarantees. Currently, I am focused on how to leverage these insights into better decision making in practice. I received my PhD in Statistical Sciences from the University of Toronto, where I was advised by Daniel Roy and affiliated with the Vector Institute. During my PhD, I was lead or co-lead author on publications in top venues including the Annals of Statistics, Journal of the American Statistical Association, Proceedings of the National Academy of Sciences, NeurIPS, and ICML. Recognitions of my work include the University of Toronto’s D. A. S. Fraser award for my contributions to statistical theory, a University of Chicago Rising Star in Data Science award, an Institute of Mathematical Statistics Hannan Graduate Student award, and NSERC funding at the undergraduate (USRA), doctoral (CGS-D), and postdoctoral (PDF) levels. Previously, I was a Research Intern at Google Brain with Been Kim, and I received my BSc in Financial Modelling from Western University. See my website for more: