Antoine Maillard

Research scientist
Inria Paris
prof_pic.jpg
Office B120
48 Rue Barrault
75013 Paris

I am a research scientist (“Chargé de Recherches”) at Inria Paris, in the ARGO team.

My research is at the intersection of high-dimensional statistics, probability theory, statistical physics, information theory and random matrix theory. You can find concrete examples of some of my recent interests in the publications section.

Before coming to Inria, I was a Hermann-Weyl Instructor at the FIM (Institute for Mathematical Research) and the department of mathematics at ETH Zürich, mentored by Afonso S. Bandeira. We started there a group blog, with an emphasis on open problems, don’t hesitate to have a look! Even before, I defended my PhD in Theoretical Physics in 2021 in Ecole Normale Supérieure de Paris, under the supervision of Florent Krzakala, and the additional guidance of Lenka Zdeborová. I am very glad to have received the 2021 Daniel Guinier Prize of the French Physical Society for it! You can find my PhD thesis here.

Motivated students are welcome to contact me for research projects, internships, or PhD positions: looking at my recent papers and teaching notes is usually a good way to see if we have similar interests. For students currently following one of my courses, please have look at the teaching section.

(Some) recent news

Dec 06, 2024 I am happy to announce that I will be joining Inria Paris on January 1st 2025, as a permanent researcher (“Chargé de Recherches”) in the ARGO team. If you want to visit or chat, my future office will be in 48 rue Barrault, 75013 Paris ! :smile:
Nov 29, 2024 I have just uploaded new lecture notes for the advanced topics course on the Mathematics of Data Science I taught at ETH during the fall of 2024! Have a look if you want to learn more (especially open questions) in diverse topics such as discrepancy theory, computational-to-statistical gaps, or the Kac-Rice formula and high-dimensional landsscapes!
Nov 29, 2024 A new version of this website is now live!
Oct 24, 2024 New preprint out on the introduction and analysis of bilinear sequence regression as a basic model for learning from sequences of tokens.
Oct 23, 2024 New preprint out on an average-case version of matrix discrepancy.