My research centers on developing and analyzing statistical methods for complex “big” datasets. On the theoretical and methodological side, I leverage and further develop results from random matrix theory for the analysis of multivariate data — e.g., via principal component analysis (PCA) — in settings where the dimension and sample size are both large. On the applied and interdisciplinary side, I have developed methods for multiple hypothesis testing motivated by questions in genomics, and used them collaboratively to make scientific discoveries about exceptional human longevity.
I've had David Donoho as my PhD advisor and collaborated with Art Owen, with Stuart Kim's lab, and with Amit Singer's group. In 2012, I obtained a BA in mathematics from Princeton University. My work is partially supported by an HHMI International Student Research Fellowship.