Jérôme Bobin is a signal and image processing researcher specializing in blind source separation, matrix factorization, compressed sensing, and machine learning. His research focuses on developing robust statistical and algorithmic methods for analyzing sparse signals and inverse problems, with applications in astrophysics, cosmology, biomedical imaging, and microscopy. His research interests include learning data representations, bias correction for astrophysical measurements, and developing optimization tools for distributed and reliable methods.