Teaching

Philosophy

I believe in open-source-based teaching, with using community codes such as Jupyter notebooks. While a part of the courses include theoretical background, I favor hands-on learning through practicals and projects. While a part of the courses include theoretical background, I favor hands-on learning through practicals and projects.

My courses rely on Jupyter for interactive computing and data visualization with Python. Jupyter notebooks provide an ideal environment for combining code, visualizations, and explanatory text in a single document, making them perfect for teaching and learning. We use JupyterHub hosted on the Dante supercomputing platform at IPGP. This infrastructure, managed by the S-CADAD facility, allows students to access computational resources directly from their web browser without any local installation, ensuring a consistent learning environment for all participants.
We use Python as the primary programming language for teaching. Python's simplicity and versatility make it an excellent choice for students of all levels, enabling them to focus on learning concepts. Additionally, Python boasts a rich ecosystem of libraries and tools that are widely used in scientific computing and data analysis. The class supports are made with Marp, a tool for creating slide decks from Markdown files. This allows for seamless integration of content and visuals, making presentations more engaging and informative.

Current classes

Scientific Computing for Geophysical Problems


This class aims at providing students with a sense of how one can use theory and computers together to test hypotheses regarding the working of a geophysical system. The testing involves the derivation of a theory with predictive power, that has to be checked against observations. Both theory and observations are impacted by uncertainty; one skill that students will acquire is the capability of assessing and propagating uncertainty in the analysis chain, through a series of computer labs specifically designed for the class. Check the online description on the IPGP website for a better overview. The course material is available on the GitHub repository and on demand.

Earth Data Sciences


This class aim to develop the way geoscientific problems can be solved with computer, ranging from rule-based programs to deep learning. Based on theoretical lessons and Jupyter Labs, the audience will learn from both theory and examples that they can re-used afterwards. Check the online description on the IPGP website for a better overview.

Programming for Geosciences


This class delivers the basics of programming in a language-agnostic way (Python, Octave, Matlab, or Julia). It aims at giving a sense on how to solve many different types of problem with a single tool: the computer. After 16 hours of theoretical courses, the class consists in a personal project developed during the class with the help of the teachers. The project can be selected from a list, or brought to the class.

Short courses and workshops

  • Fall 2024: Machine learning and artificial intelligence for geosciences (3 days). Barcelona Supercomputing Center, Barcelona, Spain. Lectures and practicals.

  • Spring 2023: An introduction to machine learning and deep learning (2 days). Réseau thématique NuTS, Lyon, France. Lectures and practicals.

  • Spring 2023: An introduction to deep learning (1 day). SPIN ITN Short Course 3, Pitlochry, Scotland. Lectures and practicals.

Former classes

  • 2019–2021: Machine Learning in Geophysics (12 hrs).
    Master of Geophysics, Grenoble-Alpes University

  • 2019–2021: Engineering Seismology (20 hrs).
    Master of Geomechanics, Civil Engineering and Risks, Grenoble-Alpes University.

  • 2017: Introduction to Algorithmic with Python* (28 hrs).
    Associate Level in Informatics, GRETA, Corbeil-Essonnes, France.
  • 2017: Passive Seismic Interferometry Practicals (4 hrs).
    Master of Geophysics, Institut de Physique du Globe de Paris
  • 2017: Modal Analysis with Musical Analogy (12 hrs).
    Bachelor of Earth and Environment, Institut de Physique du Globe de Paris.
  • 2014–2017: General Physics Practicals* (88 hrs).
    Bachelor of Environment Engineering, Denis Diderot University, Paris, France.
  • 2014–2016: Data Analysis in Earth Sciences* (84 hrs).
    Bachelor of Environment Engineering, Denis Diderot University, Paris, France.
  • 2013–2014: Scientific Programming in MATLAB* (44 hrs).
    Bachelor of Environment Engineering, Denis Diderot University, Paris, France.
  • 2013: Internet and Office Automation Certification* (22 hrs).
    Bachelor Level, Denis Diderot University, Paris, France
  • 2013: Scientific Programming in C (18 hrs).
    Master of Remote Sensing and Geomatics, Denis Diderot University, Paris, France.