Axel Faes

Background

About Axel Faes

I work at the intersection of machine learning, healthcare data, and brain-computer interfaces—specifically, how we can build AI systems that respect privacy, work across institutions, and actually deploy in clinical settings. As a postdoctoral researcher at UHasselt's Biomedical Data Sciences group and Scientific Coordinator of the Flanders AI Research Program (Use Case Real World Evidence), I focus on federated learning for healthcare applications. This means developing methods that allow hospitals and research institutions to collaborate on AI models without sharing sensitive patient data. Current projects include: Federated learning for cardiovascular risk prediction (atrial fibrillation within 6-24 months) Population health management approaches that improve disease insights while advancing health equity Brain-computer interfaces that decode imagined finger movements from intracranial recordings using tensor regression I also teach data science and bioinformatics, supervise master's and doctoral students, and try to bridge the gap between algorithmic innovation and real-world implementation—because the most elegant model means nothing if it can't navigate institutional barriers, regulatory requirements, and clinical workflows. Previously: PhD in Computational Neuroscience (KU Leuven), focused on finger movement decoding from brain signals. Open to collaborations, conversations about federated learning deployment challenges, and discussing how we make AI in healthcare actually work in practice.