Florentin Coeurdoux
👋Hi, I’m a research scientist on a quest to mechanize the scientific method by building artificial intelligence systems.
This involves working on Bayesian inference, Deep Learning, Generative models and Optimal Transport with the goal to effectively apply these to domain sciences.
About
I am a Staff Research scientist at CFM in Paris. I obtained my PhD in statistics on December 2023 at ANITI (Toulouse INP) within the SC group of the IRIT laboratory, under the supervision of Nicolas Dobigeon and Pierre Chainais . Before this, I graduated from an MEng at ENSAI in applied mathematics and computer science, and from the MBA master program of IGR in financial engineering.
In Autumn/Winter 2022/2023, I was a research visiting scholar at the Department of Statistics of the University of Oxford working with Arnaud Doucet.
Previously, I worked as a Software/Research Engineer at Credit Mutuel, leveraging machine learning to build better risk management systems. During my PhD, I was a member of the scientific advisory board for at AssessFirst
Research Interests
Deep Generative models
- Nomalizing Flows: constructing complex distributions by transforming a probability density through a series of invertible mappings.
- Diffusion based model: define a Markov chain of diffusion steps to slowly add random noise to data and then learn to reverse the diffusion process to construct desired data samples from the noise.
- Generative Adversarial Network: implicit generative modeling algorithms.
Statiscial Inference
- Density estimation: estimating the probability density function of the population from the sample.
- Likelyhood free inference: allows to evaluate posterior distributions without having to calculate likelihoods.
- Uncertainty quantification: quantifying uncertainties associated with model calculations of true, physical quantities of interest.
Optimal Transport
- Computational OT: define geometric tools that are useful to compare probability distributions.
- Sliced-Wasserstein distance: compares high-dimensional distributions by comparing their projected 1d-distributions.
Works
Journals
- F. Coeurdoux, N. Dobigeon, P. Chainais. Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference. arXiv pdf code
- F. Coeurdoux, N. Dobigeon, P. Chainais. Normalizing flow sampling with Langevin dynamics in the latent space. arXiv pdf
International Conferences
- F. Coeurdoux, N. Dobigeon, P. Chainais. Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows. ECML-PKDD 2022 arXiv pdf code press
- F. Coeurdoux, N. Dobigeon, P. Chainais. Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. ESANN 2022 arXiv pdf
National conference papers
- F. Coeurdoux, N. Dobigeon, P. Chainais. Approximation du transport optimal entre distributions empiriques par flux de normalisation. Colloque GRETSI, Sept 2022, Nancy pdf
- F. Coeurdoux, N. Dobigeon, P. Chainais. Méthode MCMC plug-and-play avec a priori génératif profond. Colloque GRETSI, Aug 2023, Grenoble pdf
Teaching
2021-2022
- Algorithms and C++ Programming - INP-ENSEEIHT (Toulouse, France)
- Optimization - INP-ENSEEIHT (Toulouse, France)
- Lebesgue integration - INP-ENSEEIHT (Toulouse, France)
- Algorithms and imperative programming - INP-ENSEEIHT (Toulouse, France)
- Statistics - INP-ENSEEIHT (Toulouse, France)
Experience
Laboratory life
Links