Marjolein Fokkema is Professor of Computational Statistics with Applications in Psychology at the Department of Statistics at TU Dortmund University and is affiliated with the Research Center Trustworthy Data Science and Security (RC Trust).
Her research lies at the intersection of statistical modeling, machine learning, artificial intelligence, psychological assessment, and psychometrics. She develops statistical methods that enable researchers to obtain interpretable scientific insights from complex machine learning models and to quantify the uncertainty associated with their results.
A central focus of her work is the development of tree-based methods and ensemble approaches. These methods can model complex patterns and differences between individuals or subgroups, but their flexibility can also make them difficult to interpret. Fokkema’s research therefore examines how their predictive strengths can be combined with transparent scientific inference and reliable uncertainty quantification.
Her work contributes to a better understanding of human behavior and has applications in areas including psychological assessment, mental health, education, and personalized treatment. She also develops open-source software for the statistical programming environment R, including the packages glmertree for generalized linear mixed-effects decision trees and pre for prediction rule ensembles.
Before joining TU Dortmund University, Fokkema worked at Leiden University, where she was Associate Professor from 2023 to 2026 and Assistant Professor from 2015 to 2023. In 2017, she was a Visiting Researcher at the University of Zurich. From 2009 to 2015, she worked as a doctoral researcher and lecturer at VU University Amsterdam.
More information is available on Marjolein Fokkema’s personal website.
Spadaccini, G., Fokkema, M., and van de Wiel, M.A. (2025). Discovery and inference beyond linearity for epidemiological data by integrating Bayesian regression, tree ensembles and Shapley values. arXiv preprint arXiv:2505.00571. DOI: 10.48550/arXiv.2505.00571
Fokkema, M., Henninger, M., and Strobl, C. (2025). One model may not fit all: Subgroup detection using model-based recursive partitioning. Journal of School Psychology, 103, 101394. DOI: 10.1016/j.jsp.2024.101394
Schwerter, J., Lauermann, F., Doebler, P., and Fokkema, M. (2025). Putting the pieces of the puzzle together in modeling gendered educational choices. International Journal of STEM Education. DOI: 10.1186/s40594-025-00558-y
Fokkema, M., & Zeileis, A. (2024). Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees. Behavior Research Methods. DOI: 10.3758/s13428-024-02389-1
van Loon, W., Fokkema, M., de Vos, F., Koini, M., Schmidt, R., & de Rooij, M. (2024). Imputation of missing values in multi-view data. Information Fusion, 111, 102524. DOI: 10.1016/j.inffus.2024.102524
Fokkema, M., Iliescu, D., Greiff, S., & Ziegler, M. (2022). Machine learning and prediction in psychological assessment: Some promises and pittfalls. European Journal of Psychological Assessment 38(3), 165-175. DOI: 10.1027/1015-5759/a000714
