dr.ir. A.Y.L. Faes (Axel)

Axel Faes is a postdoctoral researcher in AI for healthcare, specializing in federated learning, precision medicine, and brain-computer interfaces. At the University of Twente (Cognition, Data and Education), he develops foundation models and explainable AI integrating longitudinal MRI, multi-omics, and clinical data for early hepatocellular carcinoma detection within the AI-HCC ZonMw project.

Previously at Hasselt University's Biomedical Data Sciences group, he served as technical machine learning lead and scientific coordinator of the Flanders AI Research Program's Real World Evidence use case, advancing cardiovascular disease prediction and population health management through federated learning. He holds M.Sc. degrees in Computer Science Engineering and Artificial Intelligence from KU Leuven, where he also completed a PhD in Biomedical Sciences on finger movement decoding using tensor regression modeling.

His research bridges computational neuroscience, brain-computer interfaces, and privacy-preserving AI for multi-modal biomedical data, contributing to innovative methods that advance precision oncology and clinical practice.

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My research develops AI methods that bridge computational innovation with clinical translation in precision medicine. At the University of Twente, I focus on three interconnected pillars:

Foundation Models for Precision Oncology I design transformer-based architectures that capture temporal dynamics in longitudinal imaging and multi-omics data. Within the AI-HCC ZonMw project, this work targets early detection and risk stratification of hepatocellular carcinoma by learning representations that integrate MRI sequences, molecular profiles, and clinical trajectories.

Multi-Modal Federated Learning Healthcare data is inherently distributed across institutions and modalities. I develop federated learning frameworks that enable collaborative model training without centralizing sensitive patient data, preserving privacy while unlocking the statistical power of multi-institutional cohorts for robust clinical prediction.

Explainable AI for Clinical Decision Support Translating AI into clinical practice requires interpretability. I build explainable AI systems that link imaging features and molecular markers to clinically meaningful outcomes, providing oncologists with transparent, actionable insights rather than black-box predictions.

This research sits at the intersection of deep learning, biomedical data science, and clinical oncology—advancing methods that are technically rigorous, clinically relevant, and ethically grounded.

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