Recorded 14 October 2022. Aydogan Ozcan of the University of California, Los Angeles, presents "Diffractive Optical Networks & Computational Imaging Without a Computer" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: I will discuss diffractive optical networks designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially-engineered surfaces. These diffractive processors designed by deep learning have various applications, e.g., all-optical image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components such as spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) all-optical statistical inference engines, (2) computational camera and microscope designs and (3) inverse design of optical systems that are task-specific. In this talk, I will give examples of each group, enabling transformative capabilities for various applications of interest in e.g., autonomous systems, defense/security, telecommunications as well as biomedical imaging and sensing. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-i-diffractive-imaging-with-phase-retrieval/?tab=schedule