Prof. Dr. Jana-Rebecca Rehse
Assistant Professor of Management Analytics
Credit: Rike Allendoerfer
Main fields of research
- Applications of artificial intelligence in business process management
- Process mining & Process analytics
- Machine Learning for process prediction
- Domain-specific process assistance systems
- Process automation
Curriculum vitae
| 2021 | Lecturer for “Process Management Analytics”, Saarland University, Saarbrücken, Germany |
| 2020 | Junior Professor for Management Analytics, Faculty of Business Administration, University of Mannheim, Germany |
| 2015–2020 | Research Assistant, Institute for Information Systems, German Research Center for Artificial Intelligence, Saarbrücken, Germany |
| 2015–2019 | Doctorate in Business Administration (grade: summa cum laude), Saarland University, Saarbrücken, Germany (Topic: “Leveraging Artificial Intelligence for Business Process Management – A Contribution to Reference Model Mining, Predictive Process Monitoring, and Process Discovery”) |
| 2018–2019 | Lecturer for “Scientific Work”, University of Applied Sciences of Saarland, Saarbrücken, Germany |
| 2012–2015 | Master of Science in Information Systems, Saarland University, Saarbrücken, Germany |
| 2014 | Visiting Research Scholar, Center of Business Process Innovation, Howe School of Technology Management, Stevens Institute of Technology, Hoboken, New Jersey, USA |
| 2010–2012 | Bachelor of Science in Business Information Systems, Saarland University, Saarbrücken, Germany |
Prizes, awards, honors
- 2020: “Preis der Förderer”, Institute for Empirical Economic Research, Saarland University
- 2020: “Dr.-Eduard-Martin-Preis”, for the best dissertations at Saarland University
- 2016: Best Paper Nomination European Conference on Information Systems
- 2014: DAAD full scholarship “fitWeltweit” for theses abroad
Selected publications
- Grohs, M., Pfeiffer, P. and Rehse, J.-R. (2025). Proactive conformance checking: An approach for predicting deviations in business processes. Information Systems : IS, 127, 1–18.
- Abb, L. and Rehse, J.-R. (2024). Process-related user interaction logs: State of the art, reference model, and object-centric implementation. Information Systems : IS, 124, 1–16.
- Kraus, A., Rehse, J.-R. and der Aa, H. (2024). Data-driven assessment of business process resilience. Process Science, 1, 1–32.
- Plattfaut, R., Rehse, J.-R., Jans, C., Schulte, M. and van Wendel de Joode, J. (2024). Robotic process automation – research impulses from the BPM 2023 panel discussion. Process Science, 1, 1–15.
- Rehse, J.-R., Abb, L., Berg, G., Bormann, C., Kampik, T. and Warmuth, C. (2024). User behavior mining: A research agenda. Business & Information Systems Engineering : BISE, 66, 799–816.
- Rehse, J.-R., Leemans, S. J. J., Fettke, P. and Van der Werf, J. M. E. A. (2024). On process discovery experimentation: addressing the need for research methodology in process discovery. ACM Transactions on Software Engineering and Methodology, 34, 1–29.
- Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J.-R., Accorsi, R., Calvanese, D., De Giacomo, G., Fahland, D., Gal, A., La Rosa, M., Völzer, H. and Weber, I. (2023). AI-augmented business process management systems: a research manifesto. ACM Transactions on Management Information Systems, 14, 1–19.