XAI for Science Lab

I am an assistant professor in machine learning at the University of Amsterdam, where I lead the Explainable AI for Science lab. I have a joint position between the Institute for Logic, Language, and Computation, and the Informatics Institute. My group is affiliated with the Amsterdam AI for Science research center, the SIAS group, and the NLP Unit. Previously, I was a researcher at Microsoft Research AI for Science and at the Partnership on AI. My PhD in explainable ML is from the University of Amsterdam and my MSc and BSc are both in mathematics from McMaster University in Canada.

My current research interests include scientific machine learning, interpretability, and AI safety. My group works on modeling real-world systems with machine learning and developing methods for understanding such models. Our work combines methodological development in machine learning with applications in weather and climate modeling, molecular simulation, and fluid dynamics.

News

Jul 2026	Ana Lucic has been awarded an NWO Veni grant to work on mechanistic interpretability for graph ML models!
Jun 2026	We have one paper accepted to the Mechanistic Interpretability workshop at ICML 2026 on investigating what routers learn in Mixture-of-Depths models.
Feb 2026	We have one paper on investigating atmospheric structure in Aurora accepted to the Scientific Methods for Understanding Deep Learning workshop at ICLR 2026!
Nov 2025	We have two papers accepted to the Mechanistic Interpretability workshop at NeurIPS 2025! One is about equivariant sparse autoencoders, the other is about causal abstraction as a framework for faithfulness.
May 2025	Ege Erdogan has joined us as a new PhD student working on mechanistic interpretability. Welcome Ege!