Teaching

I teach graduate-level courses on statistical learning, econometrics, and financial risk management. Materials are linked where available.

Courses

2025 – present Machine Learning: Support Vector Machines

M.Sc. in Econometrics and Applied Statistics (ESA), University of Orléans — Lecturer

Classification and regression, primal and dual formulations, soft-margin SVM, kernel methods, and practical applications in Python.

Notes (EN) Notes (FR) Slides

2025 – present Interpretable Machine Learning

M.Sc. in Econometrics and Applied Statistics (ESA), University of Orléans — Lecturer

Model interpretability and explainability, model-agnostic methods, partial dependence plots, ICE, ALE, surrogate models, LIME, and Shapley values (SHAP).

Slides

2024 – present Climate Risk and Financial Risk Management

ENSAI and University of Bordeaux — Lecturer

Transmission channels from physical and transition climate risk to credit, market, and underwriting risk; implications for banking and insurance risk management and for regulatory climate stress-testing exercises.

2026 Forthcoming Deep Learning for Time Series

M.Sc. in Financial Engineering, Paris Dauphine University – PSL — Lecturer

Deep learning for time-series modelling and forecasting: recurrent and convolutional architectures, modern sequence models, attention-based methods, transformers, and recent implementations such as Kolmogorov–Arnold Networks.

2026 Forthcoming Regularization Methods

M.Sc. in Applied Mathematics and Statistics (Financial Engineering), University of Bordeaux — Lecturer

Penalized regression, Ridge, Lasso, Elastic Net, model selection, sparsity and shrinkage, the bias–variance trade-off, and practical implementation.