He received a PhD in mathematics from the University of Chicago in 2003. His research focuses on understanding structure in data, the principles of recovering such structures, and their computational, mathematical and statistical properties. His notable work includes algorithms such as Laplacian Eigenmaps and Manifold Regularization, which use ideas of classical differential geometry for analyzing non-linear high-dimensional data. He is a recipient of an NSF Career Award, and has served on editorial boards of the Journal of Machine Learning Research and IEEE PAMI.