I am a doctoral researcher specializing in interpretable machine learning for medical time series. My work focuses on applications for AI such as ease of breathing estimation for COPD and progression prediction for MS, where I design both machine learning approaches as well as novel explainability techniques.

I am currently finalizing my PhD at Ghent University-imec, after submitting my thesis entitled "Interpretable Machine Learning for Motor Evoked Potentials and Respiratory Time Series Data". I am passionate about research and development roles that focus on advancing trustworthy and reliable AI, including interpretability, uncertainty and causality, as well as working towards real utility for AI for medical and other applications.