Postdoc, University of Bordeaux
2 papers at NeurIPS 2025
We argue that Physics-Informed ML must evolve to meet the unique challenges of biological modeling, and that this evolution represents not a limitation, but a major opportunity, giving rise to Biology-Informed Machine Learning (BIML).
RCaGP is a general-purpose Gaussian Process framework that combines robustness to outliers and approximation-aware uncertainty—two coupled limitations of sparse GPs that are critical to tackle for challenging tasks like high-throughput BayesOpt.
