Demetri Psaltis
Professor of Optics and the Director of the Optics Laboratory at the Ecole Polytechnique Fédérale de Lausanne
Abstract:
Learning to perform various tasks by training neural networks has been linked to optics for a long time [1]. The remarkable progress that has been achieved in recent years with “deep learning” networks, has led to new many ideas for how to use learning techniques in the design and operation of optical systems [2,3,4,5] and vice-versa [6]. We will present results from this recent activity with particular emphasis of how deep neural networks can enhance the capabilities of optical imaging systems.
Biography:
Demetri Psaltis is Professor of Optics and the Director of the Optics Laboratory at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He was educated at Carnegie-Mellon University where he received the Bachelor of Science in Electrical Engineering and Economics in 1974, the Master’s in 1975, and the PhD in Electrical Engineering in 1977. In 1980, he joined the faculty at the California Institute of Technology, in Pasadena California where he held the Thomas G. Myers Professor of Electrical Engineering. He served as the Executive Officer for the Computation and Neural Systems department from 1992-1996. From 1996 until 1999 he was the Director of the National Science Foundation research center on Neuromorphic Systems Engineering at Caltech. At Caltech in 2004 he established the Center for Optofluidic Integration and he served as the director until he moved to EPFL in 2006 where he established his research lab and served as Dean of the engineering school for ten years. His research interests are in imaging, holography, biophotonics, nonlinear optics and optofluidics. He has over 400 publications in these areas. Dr. Psaltis is a fellow of the IEEE, the Optical Society of America, the European Optical Society and the Society for Photo- optical Systems Engineering (SPIE). He received the International Commission of Optics Prize, the Humboldt Award, the Leith Medal, the Gabor Prize and the Joseph Fraunhofer Award/Robert M. Burley Prize.
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