Education is not just about transferring knowledge — it’s about cultivating curiosity, critical thinking, and social responsibility. As a researcher and educator in computer science and data science, my mission is to train students who are not only technically skilled, but also ethically aware and societally engaged.
I believe education should be practice-driven, interdisciplinary, and research-oriented. At Hasselt University, I bring this philosophy to life through a variety of teaching roles: as a subject expert, community member, curriculum developer, project supervisor, and reflective practitioner.
As a computer scientist with a strong focus on machine learning and federated AI, I incorporate state-of-the-art methods directly into the classroom. In the Advanced Topics in Data Science course, I teach federated learning from the ground up — including the technical principles, ethical considerations, and health applications. Students complete case-based assignments using real data in interactive Jupyter environments, and feedback has been very positive.
In Bioinformatics (part of the Biomedical Sciences program), I co-teach machine learning and data representation to students with little or no computer science background. Through hands-on projects — such as detecting somatic mutations in tumors using HPC infrastructure — we support learning through coaching sessions and peer interaction. The shift in student satisfaction compared to previous years suggests that our collaborative and applied approach makes a meaningful difference.
At KU Leuven, I’m also a guest lecturer in Brain-Computer Interfacing, where I guide students through decoding experiments on EEG/ECoG data. I typically teach the final lecture of the course, presenting a real-world case study. This year’s focus was on federated learning in healthcare — aligning tightly with my own research and giving students a window into real applications of theoretical work.
Education is a shared effort. I actively contribute to a broader academic community spanning computer science, statistics, and biomedicine. Through collaborative teaching and supervision, I connect students with societal questions — from health inequity and explainable AI to FAIR data practices. Many of my thesis projects stem directly from live research contexts, ensuring students work on problems that matter.
In this spirit, I’ve helped shape curriculum content for the Informatics and Statistics & Data Science programs at UHasselt. I work on integrating new technologies, embedding projects that foster research skills, and exploring how transversal themes — like AI ethics and open data — can enrich technical training.
I tailor my teaching to my audience, blending lectures with interactive learning formats. I use notebooks, version control, and reflective assignments to help students engage both practically and conceptually. The goal is not just to understand the theory — but to apply it, critique it, and extend it.
I currently supervise multiple master’s theses across Informatics and Data Science, on topics ranging from tensor-based arrhythmia detection to time series imputation and federated modeling for global health. These projects are closely linked to my research, and in some cases lead to co-authorships, open-source contributions, or conference presentations. Supervision is never just about evaluation — it’s about feedback, iteration, and shared exploration.
One recent reflection is the shift in my role from KU Leuven to UHasselt. At Leuven, student supervision was often constrained by program structure and scale. At Hasselt, I’ve found a more personal, flexible system — built around thematic supervision and close interaction. This shift has been energizing.
Students like Denzell and Robert, who are working on compiler optimization projects, exemplify the kind of collaboration I value: deep engagement, mutual learning, and research-driven motivation. Their enthusiasm reflects the kind of academic environment I aim to help build — one where students and teachers co-create knowledge.
I am currently completing the university’s teaching qualification (BKO), with a focus on active learning and blended education. I regularly seek feedback, attend training sessions, and participate in academic communities — for example, serving as a jury member for the ACM SIGPLAN Student Research Competition at ICFP 2024.
These efforts help me stay responsive to changing educational needs and continuously improve my teaching practice.
Teaching, for me, is inseparable from research, collaboration, and social relevance. I strive to connect students to real data challenges, give them ownership over their learning, and foster an environment that is innovative, critical, and engaged. At Hasselt University’s Data Science Institute, I see education as a process of co-creation — not just with colleagues, but with students and the broader society.
Returning to Hasselt — the university where I studied myself — feels like a full-circle moment. The open atmosphere, the close-knit community, and the emphasis on student involvement were essential to my own academic development. Today, I want to give that same sense of connection and purpose back to the next generation.
I truly believe that my background and experience allow me to make a meaningful contribution to education at UHasselt — by helping students not only master the tools of data science, but also think about how and why they use them.