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. This talk will describe new microscopes that use computational imaging to enable 3D, super-resolution and phase imaging with simple and inexpensive hardware. Our reconstruction algorithms are based on large-scale nonlinear non-convex optimization with sparsity-based regularizers similar to compressed sensing.
The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features faculty doing visionary research that illustrates the character of the ongoing data, computational, inferential revolution. In this inaugural Fall 2017 "local edition," we bring forward Berkeley faculty working in these areas as part of enriching the active connections among colleagues campus-wide. All campus community members are welcome and encouraged to attend. Arrive at 3:30pm for tea, coffee, and discussion.
Laura Waller works on computational imaging and microscopy methods for biological, industrial, and commercial applications. She is an associate professor at UC Berkeley in the Department of Electrical Engineering and Computer Sciences (EECS), with affiliations in Bioengineering, QB3, and Applied Sciences & Technology. She was a postdoctoral researcher and lecturer of physics at Princeton University from 2010 to 2012 and received BS, MEng, and PhD degrees from the Massachusetts Institute of Technology in 2004, 2005, and 2010, respectively. She is a Moore Foundation Data-Driven Investigator, Bakar fellow, NSF CAREER awardee, and Packard fellow.