Latest news

  • Sep 29

    Paper accepted to Machine Learning

    An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging pdf (Machine Learning, to appear).

  • Sep 15

    Paper accepted to NeurIPS

    Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging pdf (NeurIPS 2022).

  • Sep 8

    Paper accepted to PETS

    Differentially Private Speaker Anonymization pdf (PETS 2023).

  • Aug 24

    New preprint on collaborative estimation

    Collaborative Algorithms for Online Personalized Mean Estimation pdf (arXiv:2208.11530).

  • Jul 28

    Paper accepted to IMWUT/Ubicomp

    PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning pdf (IMWUT/Ubicomp).

  • Jul 4

    New preprint on private optimization

    High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent pdf (arXiv:2207.01560).

Short bio

I am a tenured researcher at Inria, where I am part of the Magnet Team (MAchine learninG in information NETworks) and affiliated with CRIStAL (UMR CNRS 9189), a research center of the University of Lille. I am also an invited associate professor at Télécom Paris. I obtained the French habilitation thesis (HDR) in 2021.

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
  • graph-based methods
  • optimization for machine learning
  • statistical learning theory
  • fairness in machine learning
  • applications to NLP and speech recognition