- 'Interpretable Machine Learning for Modeling, Evaluating, and Refining Clinical Decision-Making' (PhD thesis)
- 'Pragmatic Policy Development via Interpretable Behavior Cloning' (arXiv preprint)
- 'Prediction Models That Learn to Avoid Missing Values' (ICML 2025)
- 'How Should We Represent History in Interpretable Models of Clinical Policies?' (ML4H 2024)
- 'Unsupervised Domain Adaptation by Learning Using Privileged Information' (TMLR 2024)
Research Experience
Since October 2025, working as a Data Scientist at Ericsson, focusing on generative AI and large language model training. During his PhD, he explored how machine learning can support decision-making in healthcare.
Education
PhD in Machine Learning from Chalmers University of Technology; MSc in Engineering Physics from Chalmers University of Technology.
Background
A machine learning researcher and engineer with a PhD from Chalmers University of Technology, focusing on how machine learning can support decision-making in healthcare. Research interests include sequential decision-making, interpretable machine learning, and off-policy evaluation.