Chair holder | Pierre-Alexandre Mattei


Pierre-Alexandre Mattei (Inria)


Short bio

Pierre-Alexandre Mattei is a Research Scientist at Inria. He is part of the Maasai (Models and Algorithms for Artificial Intelligence) team and is also affiliated with the J.A. Dieudonné lab.

His field of research is statistical machine learning, with a particular emphasis on hidden variables and model uncertainty. During his Ph.D, he mainly developed new Bayesian model selection methods for high-dimensional data. He is also currently working on deep generative models and their applications.

He is one of the co-organizers of the Workshop on the Art of Learning with Missing Values (Artemiss). 


Research topic | Deep learning for dirty data: a statistical perspective 

The successes of machine learning remain limited to clean and curated data sets. By contrast, real-world data are generally much messier. We work on designing new machine learning models that can deal with “dirty” data sets that may contain missing values, anomalies, or may not be properly normalised. Collaborators include doctors and astronomers.