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Optics and Machine Learning
USI East Campus, Sector C, Room C1.03
11:00 - 12:00
Ecole Polytechnique Federale de Lausanne (EPFL)
There is a long history linking optics and machine learning, going back to the 1980’s when optics was first used for the implementation of neural networks. The interest in the optical implementation of neural networks has been revived recently due to the explosion in the size of the networks that are realized and the associated high energy consumption required to train and operate digitally these networks. In this presentation, I will focus primarily on multimode fibers and their use as nonlinear optical computing elements. I will show that in a variety of classification tasks, the combination of nonlinear optical elements and digital co-processors  can reach classification accuracy competitive with very large digital multi-layer networks but with lower energy consumption. A possible application area of this technology is autonomous robots, vehicles and drones where low energy consumption is a critical need.
Demetri Psaltis is Professor at the Ecole Polytechnique Federale de Lausanne (EPFL). He was educated at Carnegie-Mellon University and in 1980, he joined the faculty at the California Institute of Technology, Pasadena. He moved to EPFL in 2006. His research interests are in imaging, holography, biophotonics, nonlinear optics, and optofluidics. He has authored or co-authored 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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