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I am Associate Professor of Data Science and Actuarial Mathematics at the Department of Mathematical Sciences, University of Copenhagen.

Research

I work on statistical methodology and statistical machine learning with a focus on methods that remain stable and interpretable in modern, high-dimensional data settings.

Curse of dimensionality — quick visual intuition for why nonparametric methods need structure in higher dimensions.

Interactive demo page: open here.

Research themes

Structured statistical learning: using low-dimensional structure (additivity, interactions, shape constraints) to improve stability and interpretability.
Interpretable machine learning: understanding predictions via functional decompositions, partial dependence, and structured explanations.
Survival analysis & event history models: structured nonparametric models under truncation/censoring.
Actuarial & risk applications: methodological work motivated by problems in insurance and risk management, with an emphasis on interpretable machine learning.