Laurent Demanet is Professor of Applied Mathematics, in the Department of Mathematics at MIT. He holds a joint appointment with the Department of Earth, Atmospheric, and Planetary Sciences, where he is the Director of MIT's Earth Resources Laboratory.
Previously, he was Szego assistant professor (a postdoctoral position) in the Department of Mathematics at Stanford. He obtained his Ph.D. in 2006 under Emmanuel Candes, in Applied and Computational Mathematics at Caltech. He completed his undergraduate studies in mathematical engineering and theoretical physics at Universite de Louvain, Belgium.
He is the recipient of a Sloan research fellowship, a CAREER award from NSF, and a Young Investigator award from AFOSR. His research interests include applied analysis, scientific computing, machine learning, inverse problems, and wave propagation. His group studies the mathematical and numerical challenges of inverse wave scattering.
Deep neural networks have been leveraged in surprising ways in the context of computational inverse problems and imaging over the past few years. In this talk, I will explain how deep nets can sometimes generate helpful virtual “deepfake” data that weren’t originally recorded, but which extend the reach of inversion in a variety of ways. I will discuss two examples from seismic imaging: 1) bandwidth extension, which helps to convexify the inverse problem, and 2) “physics swap”, which helps to mitigate nuisance parameters. Joint work with Hongyu Sun, Pawan Bharadwaj, and Matt Li.