Prof. Dr. Stefan Lessmann

Stefan completed his PhD and habilitation at the University of Hamburg in 2007 and 2012, respectively. He then joined the Humboldt-Universität zu Berlin in 2014, where he heads the Chair of Information Systems. He serves as an associate editor for DSS, IJDS, IJF, and other international journals and as a department editor of BISE. Stefan has secured substantial research funding and published several papers in leading international journals (EJOR, DSS, TSE, etc.) and conferences proceedings (ICML, ICIS, ECIS, etc.) His research concerns machine learning and artificial intelligence (ML/AI) methodologies and their application to inform, support, and automate decision-making in marketing and risk analytics. Commonly employed methodologies in this scope include, but are not limited to, natural language processing, causal machine learning, and procedures for explainable and responsible AI. Stefan actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations. 

Please refer to my Google Scholar profile for an up-to-date list of publications.

Studies particularly representative of my work and research interests include:

• Bokelmann, B., & Lessmann, S. (2025). Heteroscedasticity-aware stratified sampling to improve uplift modeling. European Journal of Operational Research, 325(1), 118–131. https://doi.org/10.1016/j.ejor.2025.02.030
• Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421
• Haupt, J., & Lessmann, S. (2022). Targeting customers under response-dependent costs. European Journal of Operational Research, 297(1), 369–379. https://doi.org/10.1016/j.ejor.2021.05.045
• Kozodoi, N., Jacob, J., & Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. European Journal of Operational Research, 297(3), 1083–1094. https://doi.org/10.1016/j.ejor.2021.06.023
• Kozodoi, N., Lessmann, S., Alamgir, M., Moreira-Matias, L., & Papakonstantinou, K. (2025). Fighting sampling bias: A framework for training and evaluating credit scoring models. European Journal of Operational Research, 324(2), 616–628. https://doi.org/10.1016/j.ejor.2025.01.040
• Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: Online Appendix. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030
• Lessmann, S., Haupt, J., Coussement, K., & De Bock, K. W. (2021). Targeting customers for profit: An ensemble learning framework to support marketing decision-making. Information Sciences, 557, 286–301. https://doi.org/10.1016/j.ins.2019.05.027
• Medina-Olivares, V., Lessmann, S., & Klein, N. (2024). The Deep Promotion Time Cure Model. IEEE Transactions on Neural Networks and Learning Systems, 35(12), 18848 – 18858. https://doi.org/10.1109/TNNLS.2024.3398559
• Schirmer, M., Eltayeb, M., Lessmann, S., & Rudolph, M. (2022, July 17–23). Modeling Irregular Time Series with Continuous Recurrent Units.Proceedings of Machine Learning Research Proc. of the 39th Intern. Conf. on Machine Learning (ICML'2022), PLMR, Baltimore, MD, USA.