Talk Abstract: Computational imaging is permeating cameras and microscopes across many scientific applications, enabling new high-resolution and multi-dimensional measurement capabilities (e.g. phase, 3D, hyperspectral). But many methods require acquisition of multiple images to reconstruct this new information, limiting their applicability for live dynamic samples, where motion blur can cause severe artifacts. This talk will describe new space-time algorithms that correct for motion artifacts and solve for dynamics, with imperfect optical systems or approximate forward models. Traditional model-based image reconstruction algorithms work together with neural networks to optimize the inverse problem solver and the data capture strategy.

Speaker Biography: 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.