My research interests lie at the intersection of artificial intelligence, machine learning and statistics. I am particularly interested in hierarchical probabilistic models and approximate inference/learning techniques. My current research is focused on developing probabilistic models and algorithms for learning from incomplete, uncertain and noisy multivariate time series data. My current research explores applications in both health and behavioral science based on modeling and analysis of electronic health records data as well as data collected from mobile on-body physiological sensors. In the past, I have worked on a number of additional applications including collaborative filtering and ranking, unsupervised structure discovery and feature induction, and object recognition and image labeling.
