Teaching Activities
Teaching Philosophy
Since 2020, I have been teaching optimization, artificial intelligence, and engineering modeling, primarily at ISAE-SUPAERO and Université Toulouse Capitole. My pedagogy emphasizes learning by doing: I bridge mathematical foundations with hands-on labs and concrete projects, with a strong focus on reproducibility and modern software engineering practices (Python, Jupyter, Git).
My objective is to foster scientific autonomy and critical thinking, specifically by guiding students through the fundamental trade-offs between performance, explainability, and algorithmic complexity.
Core Curriculum
Université Toulouse Capitole (MIAGE & CS)
| Subject |
Level |
Type |
Volume |
| Supervised Machine Learning | M1 | Tutorial/Project | 15h |
| Symbolic Artificial Intelligence | M1 | Tutorial/Project | 10.5h |
| Data Visualization & Unsupervised ML | M1 | Tutorial/Project | 9h |
| Python for Data Science | M1 | Tutorial | 4.5h |
Toulouse School of Economics
| Subject |
Level |
Type |
Volume |
| Introduction to Research in AI for Climate | Bachelor | Tutorial/Project | 1h |
ISAE-SUPAERO (Engineering)
| Subject |
Level |
Type |
Volume/yr |
| Numerical Analysis & Optimization | L3/M1 | Lab (Lead) | 12h |
| Bayesian Optimization for MDO | M2 | Lecture/Lab | 4h |
| Linear Programming (Simplex/PuLP) | M1 | Lab | 6h |
| Mixed-Integer Linear Programming | M1 | Lab | 4h |
| Stochastic Optimization | M2 | Tutorial | 6h |
| Complexity Theory (P/NP) | M1 | Small Class | 2h |
| Finite Element Methods | M1 | Lab | 4h |