His broad research interests are in understanding the geometry of high-dimensional data, and developing theory and methods for efficiently modeling the data. Some approaches that he has explored include sparse, kernel, graph-based and manifold methods, and more recently methods inspired by computational topology. The key application areas of his research are in data analytics and computer vision. He also has a budding interest in the interplay between humans and machines, and societal implications of machine learning.