I am a Research Engineer at Apple working on various aspects of machine learning.

I also have a part-time position as a Research Scientist at the Paul G. Allen School of Computer Science & Engineering at the University of Washington where I work with Emily Fox in the Department of Statistics and Adrian KC Lee in the Institute for Learning and Brain Sciences.

My research at UW focuses on developing statistical and machine learning methods to learn structure underlying data that arise from processes that exhibit complex dependencies such as neuroimaging data. In particular, I am interested in Bayesian nonparametric methods as well as sparse models for high-dimensional data. Additionally, I work on scalable inference algorithms for complex models of large data sets.

Previously, I was a Washington Research Foundation Innovation Postdoctoral Fellow in Neuroengineering and Data Science at the University of Washington, jointly sponsored by the Institute for Neuroengineering and the eScience Institute.

I received my PhD from the Computer Science Department at Dartmouth College where I was advised by Dan Rockmore. While in graduate schoool, I worked closely with Sinead Williamson on dependent Bayesian nonparametric models and efficient MCMC algorithms for them.