Human-inspired thinking systems can solve complex logical reasoning problems.
My research lies at the intersection of machine learning and optimization, and targets applications in computer vision and signal processing. I work at the boundary between theory and practice, leveraging mathematical foundations, complex models, and efficient hardware to build practical, high-performance systems. I design optimization methods for a wide range of platforms ranging from powerful cluster/cloud computing environments to resource limited integrated circuits and FPGAs. 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.
Here are some of my most recent projects. I believe in reproducible research, and I try to develop open-source tools to accompany my research when possible. For a full list of software and projects, see my complete research page.
Stacked U-Nets are simple, easy-to-train neural architecture for image segmentation and other image-to-image regression tasks. SUNets attain state of the art performance and fast inference with very few parameters.