Presenter

Tom's research lies at the intersection of machine learning and optimization, and targets applications in computer vision and signal processing. He works at the boundary between theory and practice, leveraging mathematical foundations, complex models, and efficient hardware to build practical, high-performance systems. He designs 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, Tom completed his PhD in mathematics at UCLA, and was a research scientist at Rice University and Stanford University. Tom has been the recipient of several awards, including SIAM’s DiPrima Prize, a DARPA Young Faculty Award, and a Sloan Research Fellowship.