Latest news

  • Oct 7

    Paper accepted to EMNLP

    Fair Without Leveling Down: A New Intersectional Fairness Definition pdf (EMNLP 2023).

  • Oct 1

    Promotion to senior researcher

    I have been promoted to Inria senior researcher (directeur de recherche in French)!

  • Aug 1

    Joining Inria Montpellier and PreMeDICaL

    After 7 great years at Inria Lille in the Magnet Team, I have moved to Inria Montpellier and PreMeDICaL Team.

  • Apr 24

    Three papers accepted to ICML

    From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning pdf, One-Shot Federated Conformal Prediction pdf and Differential Privacy has Bounded Impact on Fairness in Classification pdf (ICML 2023).

  • Feb 24

    New preprint on private optimization

    From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning pdf (arXiv:2302.12559).

  • Feb 13

    New preprint on federated learning

    One-Shot Federated Conformal Prediction pdf (arXiv:2302.06322).

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 sunny Montpellier. I am also an associate member of the Magnet Team.

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 on my LinkedIn profile.

Research interests

My main line of research is in the theory and algorithms of machine learning. I am particularly interested in designing large-scale learning algorithms that provably achieve good trade-offs between statistical performance and other key criteria such as computational complexity, communication, privacy and fairness.

My current research focus includes:

  • distributed / federated / decentralized learning algorithms
  • privacy-preserving machine learning
  • representation learning and distance metric learning
  • optimization for machine learning
  • graph-based methods
  • statistical learning theory
  • fairness in machine learning
  • applications to NLP, speech recognition and health