He received his PhD from Princeton University where he worked on problems in the domain of shape matching and shape analysis, using signal processing techniques to address problems of transformation invariant representations, model alignment, and symmetry detection. His more recent research has focused on the challenge of surface reconstruction and considers the manner in which Stokes's Theorem and Laplace's Equation can be used to efficiently and effectively reconstruct high-resolution models from oriented points. Currently, he is working on problems in the domain of image-processing, developing efficient streaming out-of-core algorithms for solving the large sparse linear systems associated with modeling images in the gradient-domain.