PhD candidate · ENS de Lyon
Maxime Munari
Machine learning, statistical learning, and signal processing for high-dimensional scientific data.
Lyon · 2025
About
I'm a physicist who works at the boundary of statistics, signal processing, and machine learning. My research targets the regime where data is high-dimensional, signal is weak, and naive evaluation leaks, the regime where most premade methods quietly fail.
Currently a PhD candidate at ENS de Lyon with Pierre Borgnat, building robust learning pipelines for biomedical mass spectrometry. Before that, computer vision for active matter, graph neural networks at CMS.
Research
machine learning
robust learning
leakage-free evaluation
weak-signal analysis
high-dimensional data
signal processing
Outside
creative coding
physics simulations
cinephile