Computational imaging involves the joint design of optical systems and postprocessing algorithms, such that computation replaces optical elements. Laura Waller gives an overview of new optical microscopes that employ simple experimental systems and efficient nonlinear inverse algorithms to achieve high-resolution 3D and phase images. By leveraging recent advances in data science, these microscopes can produce gigapixel-scale images at each time frame, computed efficiently and with good robustness to noise and model mismatch.
Laura Waller is an assistant professor at UC Berkeley in the Department of Electrical Engineering and Computer Sciences (EECS) and a senior fellow at the Berkeley Institute of Data Science (BIDS), with affiliations in Bioengineering and Applied Sciences & Technology. Previously, Laura was a postdoctoral researcher and lecturer of physics at Princeton University. Laura holds BS, MEng, and PhD degrees from the Massachusetts Institute of Technology (MIT). She is a Moore Foundation data-driven investigator, Bakar fellow, NSF CAREER awardee, and Packard fellow.
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