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.
Submitted a paper to UAI 2017 on sparse plus low-rank graphical models for time series to analyze functional connectivity from MEG data (with Rahul Nadkarni, Adrian KC Lee, and Emily Fox).
Two papers submitted to ICML 2017, one on flexible variational posteriors (with Andy Miller and Ryan Adams) and the other on stochastic gradient MCMC for HMMs (with Yian Ma and Emily Fox).
I will be giving an invited talk at the ISBA Conference on BNP in June, 2017. The talk will be on joint work with Andy Miller and Ryan Adams on flexible variational inference with variational boosting.