Paul Mangold

Presentation

I am a third year PhD student in mathematical optimization in the Magnet team at Inria Lille. My supervisors are Aurélien Bellet, Marc Tommasi and Joseph Salmon.

My research interests evolve around differentially private and distributed optimization for machine learning applications, including but not limited to:

• Differentially private empirical risk minimization.
• Optimization in general.
• Federated and decentralized learning.
• Imputation of missing data.
• Inference attacks on machine learning models.
• Fair machine learning.
• Robust statistics.

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

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

In 2019, I prepared 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.

News

• April 2023. Paper accepted to ICML 2023!
• April 2023. I am in Valencia for AISTATS 2023 to present our paper [and our poster] on high dimensional private empirical risk minimization using a greedy coordinate descent approach.
• April 2023. I am presenting a poster on the interplay of fairness and privacy at the statlearn workshop in Montpellier.

Teaching

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.