I build AI systems that learn from sensitive, multi-modal biomedical data without compromising patient privacy, and I translate them into tools clinicians can actually trust. My work sits at the intersection of three areas that have converged over the course of my career: multi-modal foundation models, privacy-preserving federated learning, and neural decoding for brain-computer interfaces. My current methodological focus is on deep learning and explainable AI (XAI) — designing architectures expressive enough to capture multi-modal biomedical signal, and interpretable enough for clinicians to rely on. The connective tissue across all three areas is a single motivating question: how do we turn high-dimensional, fragmented, privacy-constrained health data into clinically actionable insight?
At the University of Twente (CODE) and Medisch Spectrum Twente, within the AI-HCC (ZonMw) project, I develop foundation models and deep learning architectures that integrate longitudinal MRI, multi-omics profiles, and clinical variables for the early detection and risk stratification of hepatocellular carcinoma. The core challenge is temporal and cross-modal: disease signal is distributed across imaging, molecular, and clinical timelines that are individually noisy and rarely aligned. I work on transformer-based architectures that model these temporal dynamics jointly, and on explainable-AI frameworks that link imaging features and molecular markers back to clinically meaningful outcomes — a prerequisite for clinical translation in oncology.
As Scientific Coordinator of the Flanders AI Research Program’s Real-World Evidence use case and Technical Machine Learning Lead of the Biomedical Data Sciences group at UHasselt, I built federated learning frameworks that let institutions collaborate on clinical research without ever sharing raw patient data. This line of work spans federated tensor regression for longitudinal health data, distributed architectures for heterogeneous clinical datasets, and the practical translation of these methods into cardiovascular disease prediction and population health management. Privacy-preserving collaboration is, to me, the central enabler of large-scale clinical AI — it is what makes the data accessible in the first place.
My doctoral and postdoctoral research decoded finger movements and sign-language gestures from high-density intracranial recordings (ECoG). I introduced block-term tensor regression methods that capture the spatiotemporal structure of neural signals more faithfully than conventional approaches, and I studied cross-subject generalisation for robust, deployable BCIs. This work advances both the fundamental understanding of how the motor cortex encodes movement and language, and the assistive communication technology that can restore function for people who have lost it.
These themes are not separate projects but one research programme. The tensor methods I developed for neural decoding became the basis for federated regression on clinical data; that foundation now drives my work on deep learning architectures and explainable AI for multi-modal precision medicine. The explainability demands of oncology echo the interpretability I need for BCIs to be clinically credible. My contribution is to treat privacy, multi-modality, and interpretability not as constraints bolted on after the fact, but as first-class design objectives.
As an independent investigator, my goal is to establish a group at the convergence of my three themes: federated foundation models for trustworthy precision medicine. Concretely, I aim to:
Grants and projects. My research has been supported by, and I have contributed to, competitively funded programmes including an FWO Fundamental Research grant (doctoral project on finger-movement decoding), the Flanders AI Research Program – Real-World Evidence use case (Scientific Coordinator, ~10 researchers across 4 institutions), ELIXIR Belgium (consortium partner, health-data re-use and federated analyses), and the AI-HCC (ZonMw) project at the University of Twente. I was also selected — ranked 4th among international contractors — for the European Food Safety Authority (EFSA) Framework Contract in statistical and epidemiological analysis.
Open-source software. I believe methods should ship as usable tools. I author or contribute to:
Collaborations. My work is inherently multi-institutional, spanning the University of Twente, UHasselt, KU Leuven, Medisch Spectrum Twente, the University of Antwerp, and VIB.