Operator learning

Eighteen-month postdoctoral position in the Thoth team (computer vision and machine learning, Laboratoire Jean Kuntzmann, Université Grenoble Alpes), supervised by Julien Mairal. Inverse problems arise wherever images are acquired, from computational photography to microscopy, astrophysics and medical imaging: the quantity of interest is observed through a forward process (blur, noise, subsampling, sensor effects) and must be recovered from degraded measurements. Plug-and-play solvers and diffusion- or flow-based generative priors work well when the forward operator is known, but in practice i

…This models operator learning in scientific machine learning as well as computational algorithms. Randomized SVD provides strong guarantees for low-rank targets; analogous results for sparse and hierarchical structures are less developed. The talk presents progress on efficient algorithms for these classes and a broader complexity theory. Joint work with Noah Amsel, Pratyush Avi, Tyler Chen, Prathamesh Dharangutte, Chinmay Hegde, Feyza Duman Keles,