My research interests include signal processing, machine learning, and large-scale data science. Specific foci include inference from point process data, methods robust to missing data, high-dimensional data coupled with sparse and low-rank models, and streaming data.
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.