9:00 to 9:05
Welcome Address and Speaker Introduction
Bernard C. Kress, Google, United States
Jürgen Czarske, TU Dresden, Germany
2025 Symposium Chairs

9:05 to 9:55
Programming light diffraction for information processing and computational imaging

I will discuss the integration of programmable diffraction with digital neural networks. Diffractive optical networks are designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially engineered surfaces. These diffractive processors integrated with digital neural networks have various applications, e.g., image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components for spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) 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.

Dr. Aydogan Ozcan is the Chancellor’s Professor and the Volgenau Chair for Engineering Innovation at UCLA and an HHMI Professor with the Howard Hughes Medical Institute. He is also the Associate Director of the California NanoSystems Institute. Dr. Ozcan is elected a Member of the National Academy of Engineering (NAE) and a Fellow of the National Academy of Inventors (NAI) and holds >85 issued/granted patents in microscopy, holography, computational imaging, sensing, mobile diagnostics, nonlinear optics and fiber-optics, and is also the author of one book and the co-author of >1200 peer-reviewed publications in leading scientific journals/conferences.