Teaching

List of courses taught year by year:

2022/2023 (ESSEC Business School)

• Business Statistics & Analytics (ESSEC 1st year students)

• Forecasting and Predictive Analytics (for the Master in Data Sciences & Business Analytics (DSBA), ESSEC & CentraleSupélec)

• Optimization-Conscious Econometrics Summer School at the University of Chicago, June 2023.

2021/2022 (ESSEC Business School)

• Forecasting and Predictive Analytics (ESSEC Master in Management)

• Business Statistics & Analytics (ESSEC 1st year students)

• Forecasting and Predictive Analytics (for the Master in Data Sciences & Business Analytics (DSBA), ESSEC & CentraleSupélec)

2020/2021 (Harvard): Stat 248: Couplings and Monte Carlo (Spring 2021), see material here.

2019/2020 sabbatical year! I did some teaching in the Spring 2020, see the "Couplings and Monte Carlo" page.

2018/2019 (Harvard)

• STAT 131: Time series and prediction (Fall 2018)

• STAT 317: Computational Optimal Transport (Fall 2018)

• STAT 213: Statistical Inference II (Spring 2019)

2017/2018 (Harvard)

• STAT 131: Time series and prediction (Fall 2017)

• STAT 213: Statistical Inference II (Spring 2018)

2016/2017 (Harvard)

• STAT 131: Time series and prediction (Fall 2016)

• STAT 213: Statistical Inference II (Spring 2017)

• STAT 317/ CS282R: Bayesian nonparametrics with Finale Doshi-Velez (Spring 2017)

2015/2016 (Harvard)

• STAT 317: Particles in Statistics (Spring 2016, course website)

• STAT 213: Statistical Inference II (Fall 2015).

• STAT 303HFA: The Art and Practice of Teaching Statistics

2014/2015 (Oxford)

• Advanced Simulation (MSc / Part C students).

2013/2014 (Oxford)

• Grad lecture on density exploration methods: slides here.

• Advanced Simulation: 16 lectures on advanced Monte Carlo methods at the University of Oxford; other half of the course was given by Rémi Bardenet.

2011/2012 (ENSAE ParisTech)

• practical lessons in Statistics for ENSAE 2nd year students:

  • The corresponding course is given by Nicolas Chopin.

• practical lessons in Computational Stats for ENSAE 3rd year student:

  • The corresponding course is given by Christian P. Robert.

• tutoring Applied Statistics projects for ENSAE 2nd year students:

  • analyzing priceofweed's data base,

  • presence, incentives and involvement of French members of the Parliament.

2010/2011 (ENSAE ParisTech)

• practical lessons in Computational Stats for ENSAE 3rd year students.

  • The corresponding course is given by Christian P. Robert.

• practical lessons in Introduction to Statistics and Econometrics for ENSAE 1st year students.

  • The corresponding course is given by Emmanuelle Gautherat.

• tutoring Applied Statistics projects for ENSAE 2nd year students:

  • predicting Eurovision 2011 winners,

  • toponymy: finding a place's location given its name (and without google maps),

  • statistical critique of 1024 colors, a painting by Gerhard Richter.

2009/2010 (ENSAE ParisTech)

• practical lessons in Statistics for ENSAE 2nd year students.

  • The corresponding course was given by Nicolas Chopin.

• practical lessons in C++ for ENSAE 2nd year students.

  • The corresponding course was given by Matthieu Durut.

Other material:

STAT131: Time series & prediction (link to download all files in a zip archive)

• Chapter 1: introduction (notes, slides)

• Chapter 2: dependencies (notes, slides)

• Chapter 3: ARMA models (notes, slides)

• Chapter 4: parameter inference and model comparison (notes, slides)

• Chapter 5: Markov chains (notes, slides)

• Chapter 6: state space models (notes, slides)

• Chapter 7: beyond linear Gaussian models (notes, slides)

• A bunch of exercises.

• Also some R scripts corresponding to each chapter.