Vincent Plassier

Vincent Plassier

Ph.D Research Engineer

Vincent Plassier, Ph.D Research Engineer

I began a research engineer position at Huawei Technologies in the Lagrange Center at the end of February 2024, focusing on distilling diffusion models. Prior to this, I successfully defended my PhD in October 2023 at École Polytechnique, within the CMAP laboratory, under a grant from Huawei Technologies. My doctoral research was supervised by Eric Moulines and Alain Durmus.

I am currently working on federated Monte Carlo methods with applications to large-scale Bayesian inference. More broadly, I am interested in the following topics:

  • Machine learning applications
  • Distributed/Federated Monte Carlo methods
  • Uncertainty quantification via Conformal Prediction/Bayesian Fusion
  • Diffusion models

Previously, I graduated from Ecole Normale Supérieure Paris-Saclay, where I earned a master’s degree in Applied Mathematics. Additionally, I hold a master’s degree Mathematics, Vision and Learning, from ENS Paris-Saclay (MVA).

News.

  • 23/09/2024: Deliver a one-hour presentation on my work at IHES, the most prestigious research center in France (IHES website).
  • 24-27/05/2024: I presented my paper at AISTAT 2024, which took place at the Palacio de Congresos de València in Spain.
  • 05/10/2023: I defended my doctoral thesis at the École Polytechnique, France.
  • 23-29/07/2023: I presented my paper at the Hawaii Convention Center.
  • 24-27/04/2023: Presented my paper at AISTAT2023, held at Palacio de Congresos de València, Spain.
  • 24/04/2023: Excited to announce that my submission titled “Conformal Prediction for Federated Uncertainty Quantification Under Label Shift” has been accepted for inclusion in the proceedings of ICML 2023. (ArXiv)
  • 20/01/2023: Delighted to share that our paper titled “Federated Averaging Langevin Dynamics: Toward a unified theory of and new algorithms” has been accepted for AISTATS 2023. (Proceedings)
  • 13-17/03/2023: Participated in the workshop on “Statistics, Learning, Simulation, and Image” held in Hyères.
  • 24-28/10/2022: Attended an international workshop at CIRM on the Computational methods for unifying multiple statistical analyses (Bayesian Fusion).
  • 24-30/07/2022: Participated in the “Math for Machine Learning Summer School” (link) at Mohammed VI University in Ben Guerir, Morocco. I also had the opportunity to deliver a talk during the event.
  • 07-11/03/2022: Engaged in the workshop titled “New Challenges in Statistical Learning” held in Font-Romeu.
  • 01/2022: Our paper titled “QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning” has been accepted for AISTATS 2022 (Proceedings).
  • 03/2022: Collaborated with François Portier and Johan Segers on the paper “Risk bounds when learning infinitely many response functions by ordinary linear regression”. The paper has been accepted for publication in the Annales de l’Institut Henri Poincaré (ArXiv).
  • 06/2021: Attended the workshop on “Recent advances in machine learning and uncertainty” at CIRM, Marseille.
  • 05/2021: “DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs” accepted to ICML 2021 (ArXiv).