Maxime Ronceray
Astrophysics · Data Science
I develop machine-learning and statistical-inference methods for large astronomical surveys, from multimodal anomaly discovery to galaxy population modelling.
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Redshifting galaxies...
Publications
Featured
We compare AstroPT, AstroCLIP and AION representations for unsupervised discovery in matched Euclid imaging and DESI spectroscopy. A scalable density-estimation pipeline combines per-modality rarity with cross-modal misalignment to reveal model-dependent rankings, instrumental artefacts and physically coherent candidates including AGN and strong gravitational lenses.
Conferences
Talks
Documents
Design and implementation of PLASMAG, a modular simulation environment for search-coil magnetometers, including a dependency-driven simulation engine, SPICE integration and sensor-parameter optimization.
Research focus
Incoming PhD student at CEA · Astrophysics & machine learning
I am completing an MSc in Data Science and Artificial Intelligence at the University of Tours. My research focuses on reusable machine-learning representations and probabilistic methods for scientific discovery in large astronomical surveys.
I have worked on multimodal anomaly detection with Euclid and DESI data, variational inference for galaxy population priors at CEA, and scientific simulation software for space instrumentation at LPP. My foundation-model benchmarking work was accepted at the ICLR 2026 FM4Science workshop.
Elsewhere