Teaching

I teach at the intersection of computation and clinical application, where students arrive from very different backgrounds — computer scientists who have never seen a patient cohort, life scientists who have never written a model — and have to learn to work together. My teaching is built around a single conviction: students learn advanced methods best when they apply them to real, messy, consequential problems, and when the path from “I understand this” to “I can build this” is made explicit. The same principle runs through my research — I want students to build systems they can explain and others can trust, not black boxes. I have supervised 17+ Master’s and doctoral students across AI, Statistics & Data Science, Computer Science, and Biomedical Sciences, and I hold the Basic Teaching Qualification (BKO), which grounds my practice in evidence-based pedagogy.

Teaching philosophy

  • Interdisciplinarity is a skill, not an accident. My courses deliberately mix computational and biomedical students and ask them to translate for one another. Learning to explain a federated-learning protocol to a clinician — or a clinical endpoint to an engineer — is part of the assessment, not a side effect.
  • From comprehension to construction. I scaffold each topic so students move from understanding a method to implementing and critiquing it. My open-source teaching materials (below) exist so that the gap between a lecture and a working prototype is as small as possible.
  • Real data, real stakes. Whether it is the Wisconsin Breast Cancer dataset in a federated-learning tutorial or live ECoG signals in a BCI demonstration, I anchor abstract methods in problems where correctness and interpretability matter.
  • Accessibility widens the pipeline. Years of outreach coaching — teaching programming to children and to women entering tech — have convinced me that who gets to learn advanced methods is a design choice. I make my materials approachable on purpose.

Courses

Course Role Institution
Bioinformatics (3740) Coordinating lecturer (2024-2026) UHasselt
Data Science in Healthcare (4747) Lecturer, teaching team UHasselt
Advanced Topics in Data Science (4569 & 4585) — Federated Learning Lecturer, teaching team UHasselt
Brain-Computer Interfacing (H08M0A) Guest lecturer (2022-2025) KU Leuven

I have completed the BKO (Basic Teaching Qualification) — Teacher Professionalization track at UHasselt, formalising a commitment to evidence-based teaching and continuous course improvement.

Mentoring & supervision

My supervisions span four disciplines and the full range of my research — from federated machine learning and clinical prediction to programming-language design and neural decoding. Several have grown into peer-reviewed publications on which I served as senior author. Representative recent supervisions include:

  • Federated Machine Learning for Health Data — MSc Computer Science
  • Individual Reference Intervals for Clinical Event Prediction — PhD Statistics (co-supervisor)
  • Predicting Cirrhosis Patient Survival Using Machine Learning — MSc Statistics & Data Science
  • Block-Term Tensor Regression for arrhythmia detection — MSc Computer Science
  • Cross-subject finger-movement decoding from high-density ECoG — MSc Computer Science
  • Hand-exoskeleton control via a semi-invasive brain-computer interface — PhD Biomedical Sciences (daily supervision)
  • Optimization and type-checking in single-pass compilers — MSc Computer Science

A full list of supervised students is available on my About page and CV.

Educational tools & outreach

Teaching software. I build platforms that double as research and teaching infrastructure:

  • Cardinal — a clear, well-documented virtual machine and runtime designed explicitly as a platform for MSc theses in programming languages and compilers, and for prototyping new language ideas.
  • FLkit (a community toolkit I contribute to) and my Federated Learning Tutorial — guided, reproducible materials that make federated learning teachable to life scientists and students.

Outreach & service. I have coached for DjangoGirls (introducing women to programming) and CoderDojo (teaching children to code) over many years, tutored mathematics and the natural sciences, served as a Guest Associate Editor (Frontiers in Immunology), a VLAIO evaluator, and a Student Research Competition judge at ICFP. Earlier, I represented students on faculty and departmental councils at both KU Leuven and UHasselt.

Read my research statement View full CV