Nicolas Jouvin

I am a researcher at INRAE in the MIA-Paris laboratory, working in statistics and machine learning. I mainly worked in computational statistics for inference of latent variable models. Recently, I’m also interested in physics-informed machine learning methods and their applications for inverse problems and generative modeling.

From 2020 to 2021, I was a postdoc at Ecole Centrale Lyon working with Yohann De Castro on learning mixture models with sparse regularisation on the space of measures. Prior to that, I completed my PhD on high-dimensional data and graph clustering with discrete latent variable models at Paris 1 Panthéon-Sorbonne University, under the supervision of Pierre Latouche, Charles Bouveyron and Alain Livartowski.

research interests:

• Physics-informed machine learning (PINNs)
• Latent variable models
• Variational inference
• Sparse regularisation