I'm an assistant professor of Data Science and Statistics at the University of Chicago and a research scientist at Google Brain.
My research is primarily on machine learning. My recent work revolves around the intersection of machine learning and causal inference. My major interests are the design and evaluation of trustworthy AI systems, and adapting machine learning tools for learning causal effects. 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!