I am an Assistant Professor in the School of Mathematics at the University of Minnesota, Twin Cities. Prior to this, I was a postdoc in Amit Singer's research group in the Program in Applied and Computational Mathematics, Princeton University, where I was supported by the Simons Collaboration on Algorithms and Geometry. 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 problems of heterogeneity and CTF correction) and multireference alignment. I have also worked on metric approximation, specifically on fast approximations to Earth Mover's Distance on graphs and manifolds.
I am currently supported by NSF BIGDATA award IIS-1837992 and BSF award 2018230.
UMN School of Math
Yale Math Dept.