I'm an assistant professor of Statistics and Data Science at the University of Chicago (as of Jan 1, 2021) and a research scientist at Google Cambridge.
My research is primarily on machine learning. My recent work revolves around the intersection of machine learning and causal inference. I'm interested in the design and evaluation of safe and credible AI systems. Other particular interests include network data, and the foundations of learning and statistical inference.
I was previously a Distinguished Postdoctoral Researcher in the department of statistics at Columbia University, where I worked with the groups of David Blei and Peter Orbanz. I completed my Ph.D. in statistics at the University of Toronto, where I was advised by Daniel Roy. In a previous life, I worked on quantum computing at the University of Waterloo.
I like collaborations; reach out if you've got a cool problem you'd like to chat about!