Teaching

This page lists the courses I teach and my teaching materials.

Imperial College London Deep Learning module for Department of Earth Science and Engineering Master’s courses

Course webpages
Lectures – 2024/25 (Imperial students only)
Group project – 2024/25 (Imperial students only)

ETH Zürich 2024 AI in the Sciences and Engineering Master’s course

Course webpage
YouTube lecture recordings
Code used in lectures
Tutorial exercises
Course catalogue page
Course Moodle page (ETH students only)

ETH Zürich 2024 Projects in Machine Learning Research Master’s course

Lecture recording
Course catalogue page
Course Moodle page (ETH students only)

ETH Zürich 2023 Deep Learning in Scientific Computing Master’s course

Course webpage
YouTube lecture recordings
Code used in lectures
Tutorial exercises
Course catalogue page
Course Moodle page (ETH students only)

ETH Zürich 2023 Projects in Machine Learning Research Master’s course

Course catalogue page
Lecture recording
Course Moodle page (ETH students only)

Other

List of all GitHub repositories with materials from my AI and SciML workshops, tutorials and lectures

Invited lectures / workshops

• [2025] Improving the efficiency of physics-informed neural networks with domain decomposition, linear algebra, and JAX [Website].
  Autumn School on Scientific Machine Learning and Numerical Methods, Netherlands Center for Science & Informatics.
• [2025] Symbolic regression with deep neural networks [Code].
  African Institute for Mathematical Sciences, Cape Town.
• [2024] Introduction to physics-informed neural networks (mini-lecture) [Slides] [Code].
  Imperial College London.
• [2024] Introduction to JAX workshop [Slides, recording and code].
  ETH Zürich AI Center.
• [2024] Scientific machine learning and physics-informed neural networks. 
  Department of Engineering and Management Doctoral School Seminar Series, University of Padova / Lucerne University.
• [2023] Guest lecturer.
  GAMM Juniors Summer School on Scientific Machine Learning.
• [2023] How to incorporate physical understanding into machine learning [Lecture recording].
  Physical Society of Zurich.
• [2022] Scientific machine learning: ways to incorporate scientific principles into machine learning [Slides].
  University of Oxford / University of Wyoming / Lucerne University / Roche.