Teaching

Courses

• Data Visualisation, Universite Paris Dauphine – PSL, 2021–2022.
  Taught Master’s students in the IASD program, presenting data visualisation as a statistical and algorithmic tool for exploring, summarizing, and communicating complex data.

• Advanced Machine Learning, CentraleSupelec, 2024–2025.
  Served as a teaching assistant alongside Emilie Chouzenoux for this course on modern machine learning methods at the interface of statistics and optimization, with an emphasis on connecting theory, assumptions, and algorithmic practice.

• Causal Inference, New York University Paris, 2024–2025.
  Co-taught this course with Judith Abecassis, introducing the main tools of causal inference, from randomized experiments to observational and quasi-experimental methods, with a particular emphasis on observational inference.

When teaching, I like to emphasize conceptual clarity, mathematical rigor, and active engagement, with the goal of helping students understand both the structure of a method and the assumptions that make it work.

Supervision

I have been fortunate to co-supervise and mentor students on research projects:

• Julien Zhou (Master’s student, 2023)
• Samuel Girard (PhD student, 2025–2026)
• Robin Leman (Master’s student, 2026)