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.
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.