I am a Washington Research Foundation Innovation Postdoctoral Fellow in Neuroengineering and Data Science jointly sponsored by the Institute for Neuroengineering and the eScience Institute 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.
I received my PhD in computer science at Dartmouth College. My advisor was Dan Rockmore.
My research focuses on Bayesian nonparametric statistics applied to machine learning. Specifically, I work on developing dependent nonparametric priors and the associated inference algorithms for covariate-dependent latent variable models and more generally for modeling non-exchangeable data. Additionally, I am interested in large-scale inference for Bayesian nonparametric models.
Prior to Dartmouth I received my BS in computer science and mathematics from Tufts University. While at Tufts I worked with professors Soha Hassoun and Sarah Frisken.
2/4/2015 I presented a Graduation Day poster on stochastic variational inference for hidden Markov models at the 2015 Information Theory and Applications Workshop.
1/10/2015 Our paper on streaming variational inference for Bayesian nonparametric mixture models was accepted to AISTATS 2015.