Associate Professor
My research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. One central theme of my research is data-starved inference for point processes — the development of statistically robust methods for analyzing discrete events, where the discrete events can range from photons hitting a detector in an imaging system to groups of people meeting in a social network. When the number of observed events is very small, accurately extracting knowledge from this data is a challenging task requiring the development of both new computational methods and novel theoretical analysis frameworks. This body of research has led to important insights into the performance of compressed sensing in optical systems, tools for tracking dynamic meeting patterns in social network, and novel sparse Poisson intensity reconstruction algorithms for night vision and medical imaging.