Latest news

  • Aug 14

    Preprint on metric learning software

    metric-learn: Metric Learning Algorithms in Python (arXiv:1908.04710). pdf

  • Jun 17

    Paper accepted to Interspeech 2019

    Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (Interspeech 2019). pdf

  • Jun 8

    Paper accepted to ECML/PKDD 2019

    Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning (ECML/PKDD 2019). pdf

  • Feb 20

    Preprint on privacy in gossip protocols

    Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols (arXiv:1902.07138). pdf

  • Jan 25

    Preprint on decentralized machine learning

    Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph (arXiv:1901.08460). pdf

  • Jan 10

    Paper accepted to JMLR

    Kernel Approximation Methods for Speech Recognition (Journal of Machine Learning Research). pdf

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.

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 statistical machine learning. I am particularly interested in designing large-scale learning algorithms which allow a good trade-off between computational complexity (or other "resources", such as privacy or communication) and statistical performance.

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
  • applications to NLP and speech recognition