My research focuses on the theoretical and algorithmic aspects of data science, motivated by the challenge of extracting useful information from large-scale and high-dimensional data, particularly in sample-starved or resource-starved environments. I am interested exploiting and developing low-dimensional data representations that reveal geometric structures for reducing the sampling cost of information acquisition and improving the performance of decision making. Using tools in statistical signal processing, machine learning, optimization, sampling and information theory, we develop provably efficient algorithms, both statistically and computationally, for sensing and imaging applications broadly spanning network inference, inverse problems, optical imaging, and streaming data processing.

Here is a video that highlights some of our research.

Research Support

I gratefully acknowledge ongoing and past support from NSF (e.g., CCF-1901199, CCF-1806154, CAREER ECCS-1650449), AFOSR (YIP), ONR (YIP), NIH (R01), ARO, Center for Surveillance Research, ORAU, Simons Foundation, Google and Microsoft for my research group.