Tell us a little about your background: educational, professional, personal, etc.
I grew up in Cupertino, California, and then spent four years in sunny La Jolla, studying Electrical Engineering at the University of California, San Diego. At the Massachusetts Institute of Technology, I later received the Master's and Ph.D. degrees in Electrical Engineering and Computer Science. I then spent three very pleasant years in Berkeley as a postdoc, before returning to New England to join the Brown faculty in 2009.
What do you focus on in your research? Any recent advances?
My research interests span topics traditionally studied in statistics, machine learning, computer vision, and signal processing. Much of my recent work has explored vision systems which segment, recognize, and track objects in complex natural scenes. I believe data-driven, nonparametric Bayesian statistical methods provide a very promising framework to address these problems. My more abstract statistical research is typically inspired by the practical challenges of learning from large, richly structured datasets.
What do you like teaching classes about?
I love exploring the challenges of building systems which interact with noisy, messy, real-world data. Statistical methods let us apply computation to lots of exciting application areas, from object recognition to climate modeling to social networks. I also like convincing students that mathematics is both important and useful in thinking about these applications.
What is your favorite thing about Brown?
I'm excited by the strong links between different departments, and history of interdisplinary research. In machine learning, the best research problems often arise in the most unexpected places!