Imaging through scattering is arguably the most important open problem in optics. If one could overcome scattering, one could (a) see through tissue to observe "biology in action" at cellular scale; (b) see through fog, smoke, and inclement weather to safely navigate in adverse conditions; (c) see through the atmosphere and allow ground-based telescopes to outperform James Webb for a fraction of the cost; and (d) see through thin fiber bundles to enable minimally invasive endoscopy. This talk will describe how we have combined computational optics with machine learning to enable breakthrough imaging-through-scattering capabilities. I will also briefly discuss how we have applied recent advances in machine learning to improve the resolution, robustness, speed, and survivability of various sensing platforms.
Chris is an Assistant Professor in the Department of Computer Science at UMD, where he leads the UMD Intelligent Sensing Laboratory. He is a member of UMIACS and has a courtesy appointment in the Electrical and Computer Engineering Department. His research develops new systems and algorithms for solving problems in computational imaging and sensing, machine learning, and wireless communications. His work has received multiple best paper awards; he recently received NSF CAREER, AFOSR Young Investigator Program, and ARO Early Career Program awards; and he was an Intelligence Community Postdoctoral Research Fellow, an NSF Graduate Research Fellow, a DoD NDSEG Fellow, and a NASA Texas Space Grant Consortium Fellow.