Optical random features for large-scale machine learning.
Seminar "Optical random features for large-scale machine learning" by Laurent Daudet, Professor at Paris Diderot University and CTO & co-founder at LightOn on Friday November 8th at 14:00 in Pierre Cotton’s room.
Abstract :
The propagation of coherent light through a thick layer of scattering material is an extremely complex physical process. However, it remains linear, and under certain conditions, if the incoming beam is spatially modulated to encode some data, the output as measured on a sensor can be modeled as a random projection of the input, i.e. its multiplication by a random iid matrix. One can leverage this principle for compressive imaging, and more generally for any data processing pipeline involving large-scale random projections. This talk will discuss recent technological developments of optical co-processors within the startup LightOn, and present a series of proof of concept experiments in machine learning, such as transfer learning, change point detection, or recommender systems.
Biography :
As CTO at LightOn, Laurent’s task is to manage cross-disciplinary R&D projects, involving machine learning, optics, signal processing, electronics, and software engineering. Laurent is currently on leave from his position of Professor of Physics at Paris Diderot University, Paris. Prior to that or in parallel, he has held various academic positions : fellow of the Institut Universitaire de France, associate professor at Université Pierre et Marie Curie, Visiting Senior Lecturer at Queen Mary University of London, UK, Visiting Professor at the National Institute for Informatics in Tokyo, Japan, Visiting Scholar at Stanford University, USA. Laurent has authored or co-authored nearly 200 scientific publications, has been a consultant to various small and large companies, and is a co-inventor in several patents. He is a graduate in physics from Ecole Normale Supérieure and holds a PhD in Applied Mathematics from Marseille University.
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