About me
I am a postdoctoral researcher in Trustworthy Machine Learning (TML), focused on healthcare applications and reliable real-world deployment.
I earned my PhD in Computer Science from Ghent University (2023), where my dissertation centered on uncertainty quantification and robust deep learning for time-series problems. My current work advances trustworthy methods for diagnostics and patient monitoring.
Recent papers
Combining Magnetic Resonance Imaging and Evoked Potentials Enhances Machine Learning Prediction of Multiple Sclerosis Disability Worsening
TL;DR This study shows that combining MRI and evoked potentials improves machine learning prediction of disability worsening in multiple sclerosis, enabling better patient management and personalized treatment strategies.
Ising Machines for Model Predictive Path Integral-Based Optimal Control
TL;DR We show that Ising machines can be used to perform Model Predictive Control with sampling-based optimization tested on a kinematic bicycle model.
Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Progression in People with Multiple Sclerosis
TL;DR Hand-crafted radiomic features from multicenter FLAIR MRI predict disability progression in MS patients, enabling personalized treatment planning and improved outcomes.