Research
Artificial intelligence, machine learning and mathematics applied to concrete problems.
The thesis
Artificial intelligence for the optimization of the mechanical durability of systems subjected to severe vibratory loads — a military application.
Started October 2025 at Inria Rennes, funded by the Agence de l’innovation de défense and run as a consortium between the DGA, the University of Angers and the University of Rennes.
Areas
Machine learning and applied mathematics, currently around vibration fatigue and damage estimation: signal characterisation in the time, frequency and time–frequency domains, and feature engineering on non-stationary signals.
More recently, extreme value statistics.
Earlier: reinforcement learning and multi-agent systems, computer vision and remote sensing, time-series forecasting.
Reviews
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PhD Review - Time–Frequency Analysis Methods for Feature Engineering
A review of time–frequency methods for feature engineering on non-stationary vibration signals.
Inria -
PhD Review - Time Domain Methods for Fatigue and Damage Estimation
A review of time-domain fatigue estimation, from cycle counting to cumulative damage models.
Inria -
PhD Review - Frequency Domain Methods for Fatigue and Damage Estimation
A review of frequency-domain methods for fatigue and damage estimation, and the assumptions each one makes.
Inria
Earlier research
Theses, research projects and the placements they came out of.
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Apprenticeship Thesis - Leveraging Machine Learning for Dynamic and Intelligent Cloud Commitment Recommendations
Apprenticeship thesis: machine learning for dynamic and intelligent cloud commitment recommendations, at Sudo Group.
Sudo Group -
Thesis - Load-balancing and Task Allocation in Dynamic Multi-Agent Systems
Master's thesis: load-balancing and task allocation in dynamic multi-agent systems. Supervised by Pr. Maxime Morge.
Université de Lille -
IIR - Reinforcement learning and how general-purpose learning and communication methods can solve complex environments
How general-purpose learning and communication methods let reinforcement learning agents solve complex environments.
Université de Lille -
Internship - Objective Evaluation on Benchmarked Urban Environment LiDAR Data
Objective evaluation of a 3D point cloud classification algorithm on benchmarked urban LiDAR data, at CNRS OSUR.
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Internship - Semantic Segmentation State Of The Art For Ultra High Resolution UAV Imagery
Semantic segmentation for ultra-high-resolution UAV imagery: state of the art and experimental comparison, at CNRS LETG.
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Internship - Building a predictive model for road surface condition, Accenture Technology.
First engineering placement: a predictive model for road surface condition, built on an industrial Dataiku pipeline.
Accenture