About
I am currently a postdoctoral fellow in the Statistics Department of ENSAE-CREST, working with Anna Korba. I received my PhD in Mathematical Computer Science at Inria, where I was advised by Aapo Hyvärinen and Alexandre Gramfort.
My research is in machine learning. More specifically, I work on estimating and sampling energy-based models, on density-ratio estimation, and on representation learning for brain imaging data. My latest publication is on optimal distribution paths for annealed importance sampling.
You can reach me at emir.chehab [AT] ensae.fr
Publications
Self-Supervised Learning and Statistical Estimation
O. Chehab, A. Hyvärinen, A. Risteski
Spotlight, Neural Information Processing Systems (NeurIPS), 2023.
Paper Poster Code
Annealing with the right paths exponentially increases the sample-efficiency of some estimators.
O. Chehab, A. Gramfort, A. Hyvärinen
Submitted, 2023.
Paper Code
Unnormalized distributions are identified by their energy and normalizing constant. Importance sampling estimates the latter, while Noise-Contrastive Estimation estimates both: we draw a formal connection.
Deep Learning and Cognitive Neuroscience
O. Chehab*, A. Defossez*, J.C. Loiseau, A. Gramfort, J.R. King
Journal of Neurons, Behavior, Data analysis, and Theory, 2022.
Paper
A deep learning architecture best predicts brain activity caused by visual stimuli. It uses the interaction between the initial cognitive state and the visual stimulus for prediction.
H. Banville, O. Chehab, A. Hyvärinen, D. Engemann, A. Gramfort
Journal of Neural Engineering, 2021.
Paper
Self-supervised learning can produce features that correlate with sleep stages, pathology, and other neurophysiological markers.
M. Carlu*, O. Chehab*, [...], A. Destexhe, M. di Volo
Journal of Neurophysiology, 2020.
Paper
Mean-Field analysis effectively summarizes complex network dynamics that model neuronal activity.
Teaching
I was Teacher’s Assistant for the following Masters courses.
Professors: Alexandre Gramfort, Pierre Ablin
Professors: Emilie Chouzenoux, Frederic Pascal
Professors: Jean-Christophe Pesquet, Sorin Olaru, Stephane Font