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I now work at Google Research, where I do machine learning/statistical modeling/data mining/big data/current buzzword. I work on Glassbox Learning, a transparent (get it?), user-friendly, and interpretable machine learning tool. Before that, I worked on Sibyl, the largest-scale machine learning system in the world, designed to handle Google's largest data sets. I also previously worked at Google Ventures to make the venture capital investment process more quantitative and objective.

In December 2011, I finished my PhD at UC Berkeley in the Computer Science Division. I was advised by Stuart Russell and Tom Griffiths, and I worked in the Computational Cognitive Science Lab.

My PhD thesis involved using statistical machine learning to build probabilistic models of human cognition. I focused on developing nonparametric Bayesian statistical models of the ways that people learn and represent categories of objects. I also built online algorithms for dimensionality reduction problems, including Bayesian topic modeling and low-rank matrix factorization.