I am a research scientist interested in the theory and application of machine learning and statistical inference, particularly in situations where the current tools are lacking or nonexistant.
I'm currently a postdoc in the department of statistics at Columbia University, where I work 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.
Much of my work revolves around learning from relational data such as networks. Other recent interests include causal inference, stochastic optimization, privacy, and probabilistic symmetries.
I like collaborations; reach out if you've got a cool problem you'd like to chat about!