I am a post-doc in Amit Singer's research group in the Program in Applied and Computational Mathematics, Princeton University. In 2015, I completed my Ph.D in Mathematics at Yale University, working under the supervision of Ronald Coifman. In 2010 I earned my B.S. in Mathematics from the University of Chicago.
My research interests are in applied and computational harmonic analysis, statistical signal processing, and machine learning. Specifically, I develop algorithms for recovering signals that have been corrupted by both a linear filter and high additive noise. This encompasses problems with missing data, image denoising and deblurring, processing of single-cell RNA sequencing data, cryoelectron microscopy (specifically the problem of heterogeneity) and multi-reference alignment. I have also worked on metric approximation, specifically on fast approximations to Earth Mover's Distance on graphs and manifolds.
I currently receive support through the Simons Collaboration on Algorithms and Geometry.