PhD candidate in physics at ENS de Lyon, working on machine learning, statistical learning, and signal processing for high-dimensional scientific data with a focus on robust learning, leakage-free evaluation, and weak-signal analysis.
Machine learning and signal processing methods for high-dimensional mass spectrometry data in biomedical applications. Focus on weak-signal detection and robust statistical learning under distribution shift.
Computer vision and machine learning for collective-behaviour analysis and tracking of fish schools in active-matter experiments.
Graph Neural Networks in PyTorch for quark / gluon jet discrimination on simulated p–p collisions.
Analysis of galaxy distributions in simulated Euclid catalogues: redshift measurement and end-to-end error propagation.