Paul Mangold

Post-doc at Polytechnique


Since November 2023, I am a post-doctoral researcher at Ecole Polytechnique, supervised by Eric Moulines.

Before Paris, I did a PhD thesis titled Exploiting Problem Structure in Privacy-Preserving Optimization and Machine Learning, in the Magnet team at Inria Lille. My supervisors were are Aurélien Bellet, Marc Tommasi, and Joseph Salmon. During my PhD, I also worked with Michaël Perrot and Hadrien Hendrikx.

Before Lille, I was student at ENS de Lyon. I followed the Master Datasciences at Université Paris-Saclay.

In 2019, I did the agrégation de mathématiques, option informatique. This page (in french) contains all documents related to this: lessons, proofs, together with a few resources, links and remarks.

Research Interests

My research is centered around optimization, federated (reinforcement) learning, and ethical concerns that come with training machine learning models. Quite generally, I am interested in finding algorithms that can adapt to problem's structural properties to improve either convergence rate, communication complexity, or other properties like privacy and fairness.

I am also strongly interested in methods that make training and using models more practical and trustworthy. In particular in quantification of model's uncertainty, and in procedures that allow learning with missing data (e.g. imputation).

As of now, I have mostly studied convex problems, but I hope to work on more general classes of non-convex problems soon!

Feel free (and even, encouraged) to reach to me if you want to discuss any of these questions, I am always happy to chat!

You can find a list of my publications on the dedicated page or on Google scholar, and you can contact me at paul.mangold polytechnique edu. I am also on Twitter.



I teach at Lille University, where I do a machine learning course for master's students and a machine learning/graph cours for bachelor students. More details on this page.

Tools I use

I am a fervent user of emacs, and am starting to use org-mode and org-ref, that are really fantastic tools.

I generally code in C++ and python using numpy, sci-kit learn and numba. I also use tuna for profiling, which is a really nice visualisation tool.

You can find the templates of my posters and presentations on the following repository.