Her research focuses on the development of statistically- and computationally-efficient statistical machine learning methods for the analysis of high-dimensional data, with applications to genomics, neuroscience, and other fields. Given that contemporary data sets are often collected with the goal of hypothesis generation rather than hypothesis testing, she is particularly interested in unsupervised learning methods that can be used for hypothesis generation. These include methods for graphical modeling, matrix factorization, and clustering. She is the recipient of a number of honors, including an NIH Director's Early Independence Award, a Sloan Research Fellowship, and an NSF CAREER Award.