Teaching

“The scientist is not a person who gives the right answers, s/he is one who asks the right questions.”
— Claude Levi-Strauss
Translated from French; in Mythologiques, Vol. 1, Le Cru et le Cuit (1964)

I chose this quote for my PhD thesis because it reflects what I consider one of the most important, yet often neglected, goals of scientific training: cultivating curiosity and the ability to ask meaningful questions. While our academic system often rewards the recitation of established knowledge, I believe education should instead empower young researchers to explore, to question, and to innovate. We are in a transformative era of biology - with vast datasets and growing computational resources at our fingertips. However, the methods and tools to fully harness this potential are still evolving. In my teaching, I aim to inspire students not only to apply existing solutions but to create new ones. I encourage them to take risks, follow their curiosity, and experiment with novel approaches.

I encourage students to follow their passion, embrace challenges, and not be afraid to make mistakes. When teaching Python programming, Data Science, or Machine Learning, I use a hands-on, practical approach. I believe in just-in-time learning, where new concepts are immediately applied to real-world problems and projects. This way, students learn to work with messy, noisy - i.e. real biological - data rather than idealised examples. Through this, they not only understand the techniques but also learn to handle uncertainty, troubleshoot effectively, and build solutions that matter.

My goal is to help students become independent thinkers who are excited to explore, question, and innovate. Everyone can make a big difference if they are curious, persistent, and willing to ask the right questions.

Below you will find an overview of my teaching experience across courses, workshops, and supervised projects. Please feel free to contact me if you have questions, would like to discuss topics or want to share materials for learning or teaching.

Teaching Experience

Selected Lectures, Courses & Workshops

  • Advanced Biocomputing (Python Course, University of Münster, 2021 - 2025)
  • Machine Learning in Evolutionary Biology (Lecture, University of Münster, 2023 - 2024)
  • Domain-Based Phylogenetics (Lecture and Practical, University of Münster, 2023)
  • Biocomputing in Python (Workshop for the DFG-funded SPP2349, Online, 2023)
  • Linux/Shell, Git/version control and Snakemake (Workshop for the DFG-funded SPP2349, University of Münster, 2023)
  • Molecular Phylogenetics (Lecture and Practical, University of Münster, 2020 - 2023)
  • OMICS workshop (Workshop, University of Münster, 2017 - 2021)
  • Introduction to Informatics (Lecture series with Practicals, Westphalian UAS, 2015-2018)
  • Bioinformatics (Lecture series with Practicals, Westphalian UAS, 2015-2018)
  • ...

Selected Research Projects & Theses Supervised

  • Deep Learning-based Protein Domain Annotation Master's Thesis (2025)
  • Machine Learning for a New Proteome Quality Assessment Metric 3 students, Advanced Biocomputing Project (2025)
  • Protein Length Distribution as a Proteome Quality Indicator Advanced Biocomputing Project (2024)
  • Domain Annotation Improvement with Machine Learning Research Module (2024)
  • Artificial outgroup construction for Deep Learning-based phylogeny reconstruction 4 students, Advanced Biocomputing Project (2024)
  • The Impact of Domain Rearrangements in Social Insect Evolution Bachelor's Thesis (2023)
  • Statistics of Gene Co-Expression Networks of Social Insects 2 students, Advanced Biocomputing Project (2022)
  • Identifying the transcriptional regulators of two insulin-like peptides important in the maturation of termite queens Bachelor's Thesis (2021)
  • Gene-co-expression networks 2 students, Biocomputing Project (2021)
  • Distance measures for phylogeny reconstruction 2 students, Advanced Biocomputing Project (2021)
  • ...