Teaching and mentoring
Teaching is one of the most important things a scientist in academia does.
At Jena
I teach graduate courses on scientific AI, machine learning for chemistry and materials, and the critical questions AI raises for research practice.
| Course | Level | Year |
|---|---|---|
| Seminar, Current Topics in Cheminformatics | Graduate | 2025 |
| Seminar, Critical Aspects of AI | Graduate | 2025 |
I currently supervise eleven doctoral researchers and one postdoctoral researcher; 26 doctoral, master’s, postdoctoral, fellowship, and internship appointments since 2021. Alumni have gone on to graduate study in the United States and to research roles in industry.
The group runs an open mentoring programme for students outside it, and keeps how it works in a public handbook rather than as things people are expected to guess.
Open teaching material
Everything I teach is public, because the students who most need it are usually not the ones in the room.
- matextract.pub — extracting structured data from the materials literature.
- gpmbook.lamalab.org — a book on Gaussian processes and probabilistic modelling.
- ir.cheminfo.org — interactive spectroscopy tools for teaching structure elucidation.
- Lectures and exercises and live-coding notebooks from the machine learning course.
Earlier teaching
At the MolSim winter school in Amsterdam I ran a machine learning lecture and hands-on workshop in a flipped-classroom format from 2020 to 2023, built around a Kaggle competition so that students met a real generalisation gap rather than a tidy example. At EPFL I taught research data management, computational carpentry, and a doctoral course on the electronic lab notebook.
Making molecules vibrate
A virtual laboratory that lets students manipulate molecular vibrations directly, written up in the Journal of Chemical Education.