Omar Chehab is joining UW–Madison as a RISE-AI Assistant Professor in the Department of Electrical and Computer Engineering. He completed his graduate training in France, earning a PhD in Mathematical Computer Science at Inria under the supervision of Aapo Hyvärinen and Alexandre Gramfort. He subsequently held postdoctoral positions in the Department of Statistics at ENSAE/CREST, working with Anna Korba, and in the Machine Learning Department at Carnegie Mellon University, working with Pradeep Ravikumar.
His research focuses on principled methods for efficient inference from complex probability distributions. This includes estimating likelihoods from data, generating samples from unnormalized densities, as well as learning representations and discovering causal structure from brain imaging data. His work draws on a range of modern methods, including diffusion models, annealed MCMC, score matching, multi-view independent component analysis, and noise-contrastive estimation. More broadly, he studies these algorithms through the lens of computational and statistical efficiency, aiming to understand their fundamental limits and guide their design.
He regularly publishes at leading machine learning conferences such as NeurIPS, ICML, and ICLR.