My research lies at the intersection of optimization and distributed computing, and targets applications in machine learning and image processing. I design optimization methods for a wide range of platforms. This includes powerful cluster/cloud computing environments for machine learning and computer vision, in addition to resource limited integrated circuits and FPGAs for real-time signal processing. My research takes an integrative approach that jointly considers theory, algorithms, and hardware to build practical, high-performance systems. Before joining the faculty at Maryland, I completed my PhD in Mathematics at UCLA, and was a research scientist at Rice University and Stanford University. I have been the recipient of several awards, including SIAM’s DiPrima Prize, a DARPA Young Faculty Award, and a Sloan Fellowship.