Summary
Axel Faes is a postdoctoral researcher with 11 years of experience working at the intersection of machine learning, healthcare data, and brain-computer interfaces, currently driving federated learning and real-world evidence efforts across academic and clinical partners. He builds privacy-preserving, multi-institutional AI systems aimed at deployable clinical impact—projects include federated cardiovascular risk prediction, population-health methods to improve equity, and tensor-regression decoding of imagined finger movements from intracranial recordings. With a PhD in Computational Neuroscience from KU Leuven and roles at UHasselt, KU Leuven and the University of Twente, he pairs deep algorithmic expertise in multiway models and BCI with practical experience navigating regulatory and institutional barriers. He also teaches and supervises MSc/PhD students, acting as a translator between research prototypes and clinical workflows. A less obvious strength is his track record of optimizing slow, high-dimensional models for real-time use—making cutting-edge methods actually usable in practice.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Biomedical Science - Computational Neuroscience, Doctor of Philosophy - PhD Biomedical Science - Computational Neuroscience at KU Leuven
Business Summer School: United in Manchester (0739) International Business, Business Summer School: United in Manchester (0739) International Business at The University of Manchester
Bachelor of Science (B.Sc.) Computer Science, Bachelor of Science (B.Sc.) Computer Science at Hasselt University
English, Dutch, French