Short bio
I am a senior researcher (directeur de recherche) at Inria, France. I am currently part of the PreMeDICaL Team (Precision Medicine by Data Integration and Causal Learning), an Inria/Inserm research group based in Montpellier. From 2015 to 2023, I was a member of the Magnet Team (MAchine learninG in information NETworks) based in Lille.
Prior to joining Inria, I was a postdoctoral researcher at the University of Southern California (working with Fei Sha) and then at Télécom Paris (working with Stéphan Clémençon). I obtained my Ph.D. from the University of Saint-Etienne in 2012 under the supervision of Marc Sebban and Amaury Habrard.
You can find more information in my CV or my LinkedIn profile. You can also follow me on Mastodon.
Research interests
My primary research lies in the theory and algorithms of machine learning. I currently focus on trustworthy machine learning, encompassing privacy, fairness, and robustness. My work combines algorithmic design, statistical analysis, mathematical optimization, and numerical experiments. I enjoy formulating well-defined problems, designing algorithms with rigorous theoretical guarantees, and validating them on real-world tasks and datasets. Additionally, I have contributed to collaborative open-source software libraries and benchmarks.
More precisely, my research interests include:
- privacy in machine learning (with a focus on differential privacy)
- distributed / federated / decentralized learning algorithms
- fairness in machine learning
- representation learning and distance metric learning
- optimization for machine learning
- graph-based methods
- statistical learning theory
- applications to health, NLP and speech recognition