I am a second 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:
You can find a list of my publications on the dedicated page, and you can contact me at paul.mangold inria fr.
Before Lille, I was student at ENS de Lyon. I followed the Master Datasciences at Université Paris-Saclay.
J'ai passé en 2019 l'agrégation de maths option informatique, qui a donné lieu à cette page (in french) avec développements, leçons et autres maigres ressources et remarques.
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.
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 python using numpy, sci-kit learn and numba. I also use tuna for profiling, which is a really nice visualisation tool.