Title: Physical models and machine learning for Photography and Astronomy
Abstract: We live in an era of data-driven approaches to image analysis, where modeling is sometimes considered obsolete. I will propose in this talk giving back to accurate physical models of image formation their rightful place next to machine learning in the overall processing and interpretation pipeline, and discuss two applications: super-resolution and high-dynamic range imaging from raw photographic bursts, and exoplanet detection and characterization in direct imaging at high contrast. This is joint work with Theo Bodrito, Yann Dubois de Mont-Marin, Thomas Eboli, Olivier Flasseur, Anne-Marie Lagrange, Maud Langlois, Bruno Lecouat, and Julien Mairal.
Jean Ponce is a Global Distinguished Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at New York University. He is also a Professor at Ecole Normale Supérieure – PSL, where he served as Director of the Computer Science Department from 2011 to 2017. Before joining ENS and NYU, Jean Ponce held positions at Inria, MIT, Stanford, and the University of Illinois at Urbana-Champaign, where he was a Full Professor until 2005. Jean Ponce is an IEEE and an ELLIS Fellow and was a Sr. member of the Institut Universitaire de France. He has served as Program and/or General Chair of all three top international Computer Vision Conferences, CVPR (1997 and 2000), ECCV (2008) and ICCV (2023, upcoming), and as Sr. Editor-in-Chief of the International Journal of Computer Vision. He currently serves as Scientific Director of the PRAIRIE Interdisciplinary AI Research Institute in Paris. Jean Ponce is the recipient of two US patents, an ERC advanced grant, the 2016 and 2020 IEEE CVPR Longuet-Higgins prizes, and the 2019 ICML test-of-time award. He is the author of “Computer Vision: A Modern Approach”, a textbook translated in Chinese, Japanese, and Russian.