Lucas Baier

  • Kaiserstr. 89

    D - 76133 Karlsruhe


Lucas Baier is a research associate at the digital service innovation group within the Institute of Information Systems and Marketing (IISM). He is a member of the Applied AI in Services lab.



  • Machine Learning in Data Streams
  • Concept Drift Handling in Real-World Applications
  • Sustainable Application of Machine Learning Models
  • Analytics in Healthcare



If you are interested in writing a Bachelor or Master thesis, feel free to contact me at any time. 

Curriculum Vitae

Lucas Baier studied Industrial Engineering at Karlsruhe Institute of Technology during his Bachelor and Master and spent a semester abroad in Madrid. He wrote his Master thesis in cooperation with ABB about the data-driven prediction of faults in a chemical batch process


Due to the interdisciplinary activities of KSRI researchers publication lists also contain publications that have not explicitly been developed in the course of their activities at KSRI.

Conference Papers
Detecting Concept Drift With Neural Network Model Uncertainty
Baier, L.; Schlör, T.; Schöffer, J.; Kühl, N.
2023. 56th Hawaii International Conference on System Sciences 
Journal Articles
Human vs. supervised machine learning: Who learns patterns faster?
Kühl, N.; Goutier, M.; Baier, L.; Wolff, C.; Martin, D.
2022. Cognitive systems research, 76, 78–92. doi:10.1016/j.cogsys.2022.09.002
Conference Papers
Increasing Robustness for Machine Learning Services in Challenging Environments: Limited Resources and No Label Feedback
Baier, L.; Kühl, N.; Schmitt, J.
2022. Intelligent systems and applications. Vol. 1. Ed.: K. Arai, 837–856, Springer International Publishing. doi:10.1007/978-3-030-82193-7_57
Journal Articles
Conference Papers
Conceptualizing Digital Resilience for AI-Based Information Systems
Schemmer, M.; Heinz, D.; Baier, L.; Vössing, M.; Kühl, N.
2021. Proceedings of the 29th European Conference on Information Systems (ECIS), June 14 - 16, 2021 Full textFull text of the publication as PDF document
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
Baier, L.; Kellner, V.; Kühl, N.; Satzger, G.
2021. Hawaii International Conference on Systems Sciences (HICSS-54), January 5-8, 2021 
Conference Papers
Utilizing Adaptive AI-based Information Systems to Analyze the Effectiveness of Policy Measures in the Fight of COVID-19
Baier, L.; Schöffer, J.; Kühl, N.
2020. ICIS 2020 – Making Digital Inclusive: Blending the Local and the Global : December 13-16, 2020 
Handling Concept Drift for Predictions in Business Process Mining
Baier, L.; Reimold, J.; Kühl, N.
2020. 2020 IEEE 22nd Conference on Business Informatics : CBI 2020 : Antwerp, Belgium, 22-24 June 2020 : proceedings. Vol. 1, 76–83, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CBI49978.2020.00016Full textFull text of the publication as PDF document
Handling Concept Drifts in Regression Problems – the Error Intersection Approach
Baier, L.; Hofmann, M.; Kühl, N.; Mohr, M.; Satzger, G.
2020. Proceedings of 15th International Conference on Wirtschaftsinformatik, 2020, Potsdam, Germany Full textFull text of the publication as PDF document
Conference Papers
Challenges in the Deployment and Operation of Machine Learning in Practice
Baier, L.; Jöhren, F.; Seebacher, S.
2019. ECIS 2019 proceedings . 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019. Research Papers, Paper: 163, AIS eLibrary (AISeL) Full textFull text of the publication as PDF document
How to Cope with Change? Preserving Validity of Predictive Services over Time
Baier, L.; Kühl, N.; Satzger, G.
2019. Hawaii International Conference on System Sciences (HICSS-52), Grand Wailea, Maui, Hawaii, Januar 8-11, 2019, 1085–1094, University of Hawai’i at Manoa / AIS Full textFull text of the publication as PDF document