His research interests include statistical machine learning, information theory, statistical signal processing, and high-dimensional statistics. He received a PhD in EECS from the Massachusetts Institute of Technology, with a thesis focusing on graphical models and variational methods, for which he was awarded the George M. Sprowls Award for outstanding doctoral dissertation. He has received several awards, including a Sloan Fellowship, an Okawa Research Fellowship, an NSF CAREER Award, and various conference paper awards. He has worked extensively on graphical models, message-passing and variational methods, given various invited talks and tutorials on the topic, and taught a graduate level course in the area at UC Berkeley.