Chairholder | Marco Lorenzi

He is a tenured research scientist (CR) at Université Côte d’Azur, Inria Sophia Antipolis. His research interest is in the the development and study of computational and statistical methods for the analysis of biomedical data and brain images. His current research topics include Bayesian modeling and uncertainty quantification, time-series analysis, latent variable models, and federated learning.

Statistical learning in healthcare must ensure interpretability and compliance with secured data access. To tackle this problem, I will focus on 1) interpretable biomedical data modeling via probabilistic inference of dynamical systems, and 2) variational inference in federated learning for the modeling of multicentric brain imaging and genetics data.