Her research interests are in modern signal processing and data analysis, specifically in efficiently acquiring large amounts of data from few measurements, learning the inherent organization of very high-dimensional datasets via manifold learning, and effective classification of complex signals. She also regularly collaborates on applications of these issues in the fields of neuroscience and art history. Previously, she completed her PhD in 2008 at Princeton University as an NSF Graduate Fellow and Gordon Wu Fellow under the guidance of Ingrid Daubechies and Peter Ramadge.