Course entitled “Econometrics 1” at ENSAE (Graduate level) taught by Xavier D’Haultfoeuille in 2019 (Fall), 2020 (Fall) – current course website.

Teaching Assistant in 2019 (18h), 2020 (18h x 2, two groups, one in English).

Main themes: Rubin causal model, Ordinary Least Square (identification, estimation, and inference), Instrumental Variable, Panel and repeated cross-section data (Differences in Differences), and Prediction versus Causality.

Course entitled “Econometrics 2” at ENSAE (Graduate level) taught by Xavier D’Haultfoeuille in 2018 (Spring) and 2019 (Spring), taught by Michael Visser in 2020 (Spring) and 2021 (Spring) – current course website.

Teaching Assistant in 2018 (18h), 2019 (18h), 2021 (18h).

Main themes: Generalized Method of Moments, Panel data methods (First Difference, Within), Limited dependent variable: binary models (probit, logit), multinomial logit, conditional logit, count models, selection and censorship, and duration models.

Crash course in Econometrics at ENSAE (Graduate level) taught by Bertrand Garbinti in 2020 (Fall) entitled “Econométrie 3A-CI/MS” – current course website.

Teaching Assistant in 2020 (12h).

Main themes: Ordinary Least Squares (OLS); Inference and estimation under linear constraints; Standard errors and p-values; Instrumental Variable (IV) estimation; Treatment model; Panel data; Maximum Likelihood (ML) estimation and binary outcomes; Tobit and selection models.

Course entitled “Introductory Microeconomics” at ENSAE (Undergraduate level) taught by Thibaud Vergé in 2021 (Spring) – current course website.

Teaching Assistant in 2021 (24h x 2, two groups).

Main themes: consumer and producer theory; partial and general equilibrium; uncertainty (Von Neumann-Morgenstern); imperfect competition.

Course entitled “Statistics 1” at ENSAE (Graduate level) taught by Nicolas Chopin in 2017 (Fall) and 2018 (Fall), Arnak Dalalyan in 2019 (Fall) and 2020 (Fall) – current course website.

Teaching Assistant in 2017 (18h), 2018 (18h), 2019 (18h), and 2020 (18h, in English).

Main themes: Statistical models (identification, parametric/nonparametric), Estimation (Method of Moments, Maximum of Likelihood, Bayes), Confidence intervals, Parametric tests, Adequation tests, Introduction to density estimation (kernel methods, Nadaraya-Watson), and Computational statistics (Newton-Raphson, EM Algorithm, Gibbs-sampling, Bootstrap).

Course entitled “Mathematical Statistics” at ENSAE (Graduate level – crash course for parallel admission students) taught by Guillaume Lecué in 2018 (Fall), 2019 (Fall), and 2020 (Fall) – current course website – link to course material.

Teaching Assistant in 2018 (12h), 2019 (12h) and 2020 (12h).

Main themes: Statistical models, Plug-in approach, Probability basic tools (LLN, CLT, Delta-method), Z- and M-estimation and asymptotic inference, Comparison of estimators (Cramer-Rao bounds), and Parametric tests (Neyman-Pearson).

Course entitled “Introductory Statistics” at ENSAE (Undergraduate level) taught by Marco Cuturi in 2018 (Spring), Arnak Dalalyan in 2019 (Spring), now by Matthieu Lerasle – current course website.

Teaching Assistant in 2018 (24h) and 2019 (24h).

Main themes: Statistical models (identification, parametric), Estimation (Method of Moments, Maximum of Likelihood), Confidence intervals, Parametric tests, Adequation tests, and Application in R (tutorials).

Course entitled “Analysis” at ENSAE (Undergraduate level) taught by Laurent Decreusefond in 2020 (Fall) – current course website – link to course material.

Teaching Assistant in 2020 (24h).

Main themes: topological space, Hilbert space, Fourier series.

Oral examiner for Serge Nicolas in the B/L preparatory class at Lycée Henri IV, Paris.

2h weekly in academic years 2014-2015 and 2015-2016.

Main themes: Real and complex analysis, Linear algebra, and Probability.