Where: Seminarroom of PI4, room 4.319
Abstract:
Computational imaging involves the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. Computers can replace bulky and expensive optics by solving computational inverse problems, or new imaging modalities can be enabled by reconstructing invisible quantities or higher-dimensional information from carefully-designed measurement. This talk will describe end-to-end learning for development of new microscopes and space-time algorithms that use computational imaging to enable 3D phase and fluorescence measurement with high resolution and dynamic samples. We demonstrate these concepts with a programmable-illumination microscope having an LED array for illumination, or a system with patterned illumination achieved with random diffusers (Scotch Tape). Traditional model-based image reconstruction algorithms are based on large-scale nonlinear non-convex optimization; we combine these with neural networks to learn both the image and its algorithmic self-calibration.
Bio:
Laura Waller is the Charles A. Desoer Professor of Electrical Engineering and Computer Sciences at UC Berkeley. She received B.S., M.Eng. and Ph.D. degrees from the Massachusetts Institute of Technology in 2004, 2005 and 2010. After that, she was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012. She is a Packard Fellow for Science & Engineering, Moore Foundation Data-driven Investigator, OSA Fellow, and Chan-Zuckerberg Biohub Investigator. She has received the Carol D. Soc Distinguished Graduate Mentoring Award, OSA Adolph Lomb Medal, the SPIE Early Career Award and the Max Planck-Humboldt Medal.