About

I’m a PhD candidate at Ghent University working on causal machine learning for data-driven decision support.

Research interests

• Causal inference, treatment effect estimation and policy learning
• Orthogonal or debiased machine learning methods
• Applications in healthcare and business

My recent work includes “Rank-Learner: Orthogonal Ranking of Treatment Effects”, which introduces a Neyman-orthogonal learning objective for directly ranking individuals by their treatment effects. Developed in collaboration with LMU Munich, the paper was accepted at ICML 2026 (arXiv). In addition, “Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records” was accepted at Machine Learning for Healthcare (MLHC) 2026 (arXiv).

More broadly, I am interested in how AI systems can be designed and evaluated to support real-world decision-making, and in understanding when these systems create value in practice.

Outside of research, I enjoy endurance sports and serve as vice-president of my local triathlon club, and I’m a big fan of specialty coffee. 🏃☕