Dr. Philip Stahmann

Werdegang

• 10/2012 - 02/2016: Bachelor European Business Programme (EBP), Fachhochschule Münster und Universidad Antonio de Nebrija, Madrid.
• 10/2016 - 12/2018: Master of Science in Information Systems, Westfälischen Wilhelms-Universität Münster.
• 01/2019 - 12/2019: BI & Analytics Associate, Sopra Steria SE.
• 01/2020 - 03/2024 Wissenschaftlicher Beschäftigter am Lehrstuhl für Management Support und Wirtschaftsinformatik, Universität Osnabrück.  
• Seit 2022 Wissenschaftlicher Beschäftigter am Lehrstuhl für Enterprise Computing.
• 03/2024 - Promotion in KI-basierter Datenanalyse in der produzierenden Industrie.

Forschungsinteressen

• Sozio-technische Konzeption und Implementierung von künstlicher Intelligenz in der betrieblichen Anwendung 
• Methoden der künstlichen Intelligenz, insb. des Machine und Deep Learning, zur Datenanalyse in den Bereichen Industrie und Medizin

Publikationen

2025

• Lenssen, L., Stahmann, P., Schubert, E., Janiesch, C. (2025): Archetype Discovery from Taxonomies: A Method to Cluster Small Datasets of Categorical Data. Proceedings of the 57th Hawaii International Conference on System Sciences 2025 (Forthcoming).
• Stahmann, P., Röver, J., Rodda, A., Ciftci, S. and Janiesch, C. (2025): Opportunities and Challenges for AI-supported Business Intelligence Systems – A Delphi Study. Proceedings of the 57th Hawaii International Conference on System Sciences 2025 (Forthcoming).
• Stahmann, P., Zemke, H. and Janiesch, C. (2025): Leveraging Generative Artificial Intelligence and Design Thinking in Creative Processes: A Literature Review. Proceedings of the 57th Hawaii International Conference on System Sciences 2025 (Forthcoming).

2024

• Mayr, A., Stahmann, P., Nebel, M. et al. Still doing it yourself? Investigating determinants for the adoption of intelligent process automation. Electron Markets 34, 56 (2024). doi.org/10.1007/s12525-024-00737-9
• Nebel, M., Stahmann, P., Janiesch, C. (2024): Development and Future Research Directions of AI-based Anomaly Detection in Manufacturing - A Bibliometric Analysis. Proceedings of the International Conference on Wirtschaftsinformatik 2024. aisel.aisnet.org/wi2024/102

2023

• Mayr, A., Stahmann, P., Nebel, M., Janiesch, C. (2023): Unified Theory of Acceptance and Use of Technology (UTAUT) for Intelligent Process Automation. Proceedings of the 44th International Conference on Information Systems 2023. aisel.aisnet.org/icis2023/itadopt/itadopt/6
• Rodda, A. and Stahmann, P. (2023): Towards a Student-Centered Learning Analytics Dashboard: Design, Development and Evaluation. Proceedings of the 29th Americas Conference on Information Systems 2023. aisel.aisnet.org/amcis2023/sig_ed/sig_ed/17
• Stahmann, P. (2023): A Prototypical Dashboard for Knowledge-Based Expert Systems used for Real-Time Anomaly Handling in Smart Manufacturing. Proceedings of the 56th Hawaii International Conference on System Sciences 2023. aisel.aisnet.org/hicss-56/os/data_analytics/4
• Stahmann, P. and Rieger, B. (2023): A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0., in: G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, P. Pardalos, G. Di Fatta, G. Giuffrida and R. Umeton (eds.), Machine Learning, Optimization, and Data Science (LOD). LNCS, Vol. 13810. Cham: Springer Nature Switzerland. link.springer.com/chapter/10.1007/978-3-031-25599-1_3
• Stahmann, P., Gravemeier, L. S., Dittmer, A., Janiesch, C. and Thomas, O. (2023): User- Centered Visual Design of Alarms in Production Dashboards: Insights on Comprehensibility and Preferences. Proceedings of the 44th International Conference on Information Systems 2023. aisel.aisnet.org/icis2023/hti/hti/4
• Stahmann, P., Nebel, M. and Rieger, B. (2023): A Real-Time Semantic Anomaly Labeler Capturing Local Data Stream Features to Distinguish Anomaly Types in Production, in: G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, P. Pardalos, G. Di Fatta, G. Giuffrida and R. Umeton (eds.), Machine Learning, Optimization, and Data Science (LOD). LNCS, Vol. 13810. Cham: Springer Nature Switzerland. link.springer.com/chapter/10.1007/978-3-031-25599-1_30
• Stahmann, P., Rodda, A. and Janiesch, C. (2023): The Effect of Advanced Analytics Real- Time Dashboards on Cognitive Absorption and Task Load of Human End Users. Proceedings of the 31st European Conference on Information Systems 2023. aisel.aisnet.org/ecis2023_rp/291

2022

• Stahmann, P., Oodes, J. and Rieger, B. (2022): Improving Machine Self-Diagnosis with an Instance-Based Selector for Real-Time Anomaly Detection Algorithms, in: Cabral Seixas Costa, A.P., Papathanasiou, J., Jayawickrama, U., Kamissoko, D. (eds.), Decision Support addressing modern Industry, Business and Societal needs. LNBIP, Vol. 447. Springer, Cham. link.springer.com/chapter/10.1007/978-3-031-06530-9_3
• Stahmann, P. and Rieger, B. (2022): Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data. Proceedings of the 55th Hawaii International Conference on System Sciences 2022. aisel.aisnet.org/hicss-55/os/risks/4/

2021

• Stahmann, P. and Krüger, A. (2021): Digital Twins for Real-time Data Analysis in Industrie 4.0: Pathways to Maturity. Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics 2021. 
• Stahmann, P. and Rieger, B. (2021): Requirements Identification for Real-Time Anomaly Detection in Industrie 4.0 Machine Groups: A Structured Literature Review. Proceedings of the 54th Hawaii International Conference on System Sciences 2021. aisel.aisnet.org/hicss-54/os/risks/4/

Online-Profile

• LinkedIn
• ResearchGate