Title: Computational Imaging of Space, Spectrum, and Phase
Abstract: The vast majority of sensing systems rely on non-adaptive measurements. Yet, there are immense benefits to be derived by adapting the measurement process to the specifics of the scene being sensed, including improved SNR and conditioning, as well as reduction in the number of measurements and the computational footprint of the reconstruction. This talk covers recent results that illustrate the benefits of adaptivity in hyperspectral imaging and wavefront sensing.
Bio: Aswin C. Sankaranarayanan is an associate professor in the ECE department at CMU. His research interests are broadly in computational imaging, signal processing and computer vision. Aswin did his doctoral research at the University of Maryland where his dissertation won the distinguished dissertation award from the ECE department in 2009. He is the recipient of the CVPR 2019 best paper award, the CIT Dean’s Early Career Fellowship, the NSF CAREER award, the Spira Teaching award, the Eta Kappa Nu (CMU Chapter) Excellence in Teaching award, and the Herschel Rich Invention award from Rice University.
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo