Robin Hirt

Dr.-Ing. Robin Hirt



From January 2017 to September 2019, Robin Hirt was a researcher as part of the Applied AI Lab where he also obtained his PhD. During this time, he worked at and collaborated with the renowned MIT-IBM AI Lab in Boston in the area of Sequential Transfer Learning. His research focuses on the development of artificial intelligence based on distributed data sets while taking data confidentiality into account. Mr. Hirt solved theoretical as well as practical problems in several fields of application with artificial intelligence using design oriented methods.

In his research, he combined Meta and Transfer Machine Learning to enable "learning" in systems of complex companies. Hereby, his work on sequential transfer learning set ground for future endeavors in the area of federated data analysis. In 2018, he founded prenode GmbH to bring his research from the field of Applied AI into practice as managing director and chief technical officer. prenode develops a solution for training machine learning models on federated data sources without compromising data privacy.


  • Meta Machine Learning
  • Transfer Machine learning
  • Federated Machine Learning
  • Complex Event Processing, Stream Analytics
  • Explainable AI


  • Digital Services (SS19)
  • Artificial Intelligence in Service Systems (WS18/19)
  • Foundations of Digital Services A (SS18)
  • Foundations of Digital Services A (SS17)
  • Seminar: Applied Machine Learning and Microservices (SS17)


Conference Papers
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting.
Hirt, R.; Kühl, N.; Peker, Y.; Satzger, G.
2020. IEEE International Conference on Business Informatics (CBI), IEEE 
A network-based transfer learning approach to improve sales forecasting of new products.
Karb, T.; Kühl, N.; Hirt, R.; Glivici-Cotruță, V.
2020. European Conference on Information Systems (ECIS) - Marrakech, Marocco, June 15 - 17, 2020 
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime.
Walk, J.; Hirt, R.; Kühl, N.; Hersløv, E. R.
2020. Exploring Service Science : 10th International Conference on Exploring Service Science, IESS 2020, Porto, Portugal, February 05-07, 2020. Proceedings. Ed.: H. Nóvoa, Springer International Publishing, Basel Full textFull text of the publication as PDF document
Journal Articles
Conference Papers
Service Systems, Smart Service Systems and Cyber-Physical Systems—What’s the difference? Towards a Unified Terminology.
Martin, D.; Hirt, R.; Kühl, N.
2019. 14. Internationale Tagung Wirtschaftsinformatik 2019 (WI 2019), Siegen, Germany, February 24-27 
Machine Learning in Artificial Intelligence: Towards a Common Understanding [in press].
Kühl, N.; Goutier, M.; Hirt, R.; Satzger, G.
2019. Hawaii International Conference on System Sciences (HICSS-52), Grand Wailea, Maui, Hawaii, Januar 8-11, 2019 
How to Learn from Others? A Research Agenda on Transfer Machine Learning for Sales Forecasting.
Hirt, R.; Kühl, N.
2019, February 19. MIT-IBM Watson AI Lab (2019), Cambridge, MA, USA, March 19, 2019 
Conference Papers
Cognition in the Era of Smart Service Systems: Inter-organizational Analytics through Meta and Transfer Learning.
Hirt, R.; Kühl, N.
2018. 39th International Conference on Information Systems, ICIS 2018; San Francisco Marriott MarquisSan Francisco; United States; 13 December 2018 through 16 December 2018, AIS, New York (NY) 
Towards Service-oriented Cognitive Analytics for Smart Service Systems.
Hirt, R.; Kühl, N.; Schmitz, B.; Satzger, G.
2018. Hawaii International Conference on System Sciences (HICSS-51), Waikoloa Village, Hawaii, United States, 3rd - 6th January 2018 
Conference Papers
How to Cope With Incomplete Prediction Input? A Categorization of Techniques For Realizing Robust Analytics for Smart Service Systems.
Hirt, R.
2017. 3rd Karlsruhe Service Summit Research Workshop, Karlsruhe, Germany, 21st - 22nd September 2017 
Abbildung kognitiver Fähigkeiten mit Metamodellen.
Hirt, R.; Kühl, N.
2017. INFORMATIK 2017, 47. Jahrestagung der Gesellschaft für Informatik, Chemnitz, Deutschland, 25. - 29. September 2017. Hrsg.: Maximilian Eib, 2301–2307, Gesellschaft für Informatik e.V., Bonn. doi:10.18420/in2017_231Full textFull text of the publication as PDF document
An End-to-End Process Model for Supervised Machine Learning Classification : From Problem to Deployment in Information Systems.
Hirt, R.; Kühl, N.; Satzger, G.
2017. Designing the Digital Transformation, DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology, Karlsruhe, Germany, 30th May - 1st June 2017. Ed.: A. Mädche, 55–63, KIT, Karlsruhe