Bild: Karin Glückler

Prof. Dr. Christian Bartelt

Assoziierter Professor des Institut für Enterprise Systems

Universität Mannheim
Institut für Enterprise Systems
L 15, 1–6 – Raum 417
68161 Mannheim

Tel.: +49 152 0 2639807
E-Mail: christian.barteltuni-mannheim.de
Web: www.uni-mannheim.de/ines/ueber-uns/wissenschaftliche-mitarbeiterinnen-und-mitarbeiter/dr-christian-bartelt

Forschungsinteressen

Software technology: Architectures and technologies to ensuring dependability of smart and dynamic software systems
Process management: Model-based engineering of smart processes in business, production and development of enterprises
Software systems engineering: Artificial intelligence applications in software and systems engineering

Lebenslauf

Seit 2015	Leiter des Forschungsclusters „Intelligent Processes“ am InES (Universität Mannheim)
Seit 2012	Dozent an der Technischen Universität Clausthal
Seit 2011	Dozent an der Universität Hildesheim
2013–2014	Koordinator of the Cooperative Doctorial Program E-Mobility (University of Braunschweig, Clausthal, Hannover, Ostfalia)
2011–2015	Postdoc am Institute for Software and Systems Engineering (Technische Universität Clausthal )
2007–2011	Wissenschaftlicher Mitarbeiter am Institute for Software and Systems Engineering (Technische Universität Clausthal)
2004–2007	Wissenschaftlicher Mitarbeiter am Lehrstuhl Software Architecture (Technische Universität Kaiserslautern)
1997-2004	Studium Computer Science an der Technischen Universität Kaiserslautern

Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2024). Investigating demographic features and their connection to performance, predictions, and fairness in EDM models. Journal of Educational Data Mining, 16, 177–213.
Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. und Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 113, 3633-3652.
Rink, J., Tollens, F., Tschalzev, A., Bartelt, C., Heinzl, A., Hoffmann, J., Schoenberg, S. O., Marzina, A., Sandikci, V., Wiegand, C., Hoyer, C. und Szabo, K. (2024). Establishing an MSU service in a medium-sized German urban area — clinical and economic considerations. Frontiers in Neurolgy, 15, 1–9.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2023). Correction to: Data-driven prototyping via natural- language-based GUI retrieval. Automated Software Engineering, 30, 1–2.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2023). Data-driven prototyping via natural-language-based GUI retrieval. Automated Software Engineering, 30, 1–34.
Burzlaff, F., Wilken, N., Bartelt, C. und Stuckenschmidt, H. (2022). Semantic interoperability methods for smart service systems: A survey. IEEE Transactions on Engineering Management : EM, 69, 4052-4066.
Marton, S., Lüdtke, S. und Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
Niemann, F., Lüdtke, S., Bartelt, C. und Ten Hompel, M. (2022). Context-aware human activity recognition in industrial processes. Sensors, 22, 1–14.

Kretzer, F., Kolthoff, K., Bartelt, C., Ponzetto, S. P. und Maedche, A. (2025). Closing the loop between user stories and GUI prototypes: an LLM-based assistant for cross-functional integration in software development. In , CHI '25 : proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (S. 1–19). , Association for Computing Machinery: New York, NY, USA.
Marton, S., Grams, T., Vogt, F., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2025). Mitigating information loss in tree-based reinforcement learning via direct optimization. In , The Thirteens International Conference on Learning Representations (S. ). , OpenReview.net: .
Marton, S., Schneider, M., Brinkmann, J., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2025). Decision trees that remember: Gradient-based learning of recurrent decision trees with memory. In , The Thirteens International Conference on Learning Representations (S. ). , OpenReview.net: .
Klerings, A., Bartelt, C. und Mueller, A. (2024). Developmentally plausible multimodal language models are highly modular. In , The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning : proceedings of the Second BabyLM Challenge : November 15–16, 2024 (S. 118–139). , Association for Computational Linguistics: Miami, FL, USA.
Kolthoff, K., Bartelt, C., Ponzetto, S. P. und Schneider, K. (2024). Self-elicitation of requirements with automated GUI prototyping. In , Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering : ASE 2024 : October 28 – November 1, 2024, Sacramento, California, USA (S. 2354-2357). , IEEE/ACM: Sacramento, CA, USA.
Kolthoff, K., Kretzer, F., Bartelt, C., Maedche, A. und Ponzetto, S. P. (2024). Interlinking user stories and GUI prototyping: A semi-automatic LLM-based approach. In , 2024 IEEE 32nd International Requirements Engineering Conference (RE) (S. 1–9). , IEEE: Reykjavik, Iceland.
Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GRANDE: Gradient-Based Decision Tree Ensembles for tabular data. In , International Conference on Learning Representations (S. 1–27). , OpenReview.net: .
Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. In , Proceedings of the 38th AAAI Conference on Artificial Intelligence (S. 14323-14331). , AAAI Press: Washington, DC.
Oesterle, M., Grams, T. und Bartelt, C. (2024). DRAMA at the PettingZoo: Dynamically restricted action spaces for multi-agent reinforcement learning frameworks. In , Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024, Hilton Hawaiian Village Waikiki Beach Resort, Hawaii, USA, January 3–6, 2024 (S. 7810-7819). , Department of IT-Management, Shidler College of Business, University of Hawaii: Honolulu, HI.
Oesterle, M., Grams, T., Bartelt, C. und Stuckenschmidt, H. (2024). RAISE the bar: Restriction of action spaces for improved social welfare and equity in traffic management. In , Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (S. 1492-1500). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
Tschalzev, A., Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). A data-centric perspective on evaluating machine learning models for tabular data. In , The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (S. 1–35). , NeurIPS: Vancouver, BC.
Tschalzev, A., Nitschke, P., Kirchdorfer, L., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). Enabling mixed effects neural networks for diverse, clustered data using Monte Carlo methods. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence: Jeju, 03–09 August 2024 (S. ). , International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
Wilken, N., Cohausz, L., Bartelt, C. und Stuckenschmidt, H. (2024). Fact Probability Vector Based Goal Recognition. In , 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (S. 4254-4261). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam [u. a.].
Brinkmann, J., Swoboda, P. und Bartelt, C. (2023). A multidimensional analysis of social biases in vision transformers. In , Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (S. 4914-4923). , IEEE: Paris, France.
Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.
Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Outlying aspect mining via sum-product networks. In , Advances in knowledge discovery and data mining: 27th Pacific-Asia Conference on knowledge discovery and data mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023 : proceedings. Part I (S. 27–38). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). GradTree: Learning axis-aligned decision trees with gradient descent. In , (S. 1–17). , Neural Information Processing Systems Foundation, Inc. (NeurIPS): New Orleans.
Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Online random feature forests for learning in varying feature spaces. In , Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol. 4 (S. 4587-4595). , AAAI Press: Washington, DC.
Wilken, N., Cohausz, L., Bartelt, C. und Stuckenschmidt, H. (2023). Planning landmark based goal recognition revisited: Does using initial state landmarks make sense? In , KI 2023: Advances in Artificial Intelligence : 46th Conference on AI, Berlin, Germany, September 26–29, 20023, proceedings (S. 231–244). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Exchangeability-aware sum-product networks. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, 23–29 July 2022 (S. 4864-4870). , International Joint Conferences on Artificial Intelligence Organization: Wien.
Oesterle, M., Bartelt, C., Lüdtke, S. und Stuckenschmidt, H. (2022). Self-learning governance of black-box multi-agent systems. In , Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV : International Workshop, COINE 2022, virtual event, May 9, 2022, revised selected papers (S. 73–91). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2022). Dynamic forest for learning from data streams with varying feature spaces. In , Cooperative information systems : 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (S. 95–111). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.
Burzlaff, F. und Bartelt, C. (2021). Knowledge-driven architecture composition: Assisting the system integrator to reuse integration knowledge. In , ICWE 2021 : 21st International Conference on Web Engineering, Biarritz, France, May 18–21, 2021 ; proceedings (S. 305–319). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Hoffmann, L., Bartelt, C. und Stuckenschmidt, H. (2021). Knowledge injection via ML-based initialization of neural networks. In , Proceedings of the CIKM 2021 Workshops (CIKMW 2021) co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) : Gold Coast, Queensland, Australia, November 1–5,2021 (S. 1–6). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2021). Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements. In , NLDB 2021 : 26th International Conference on Natural Language & Information Systems, Saarbrücken, Germany, June 23–25, 2021 ; proceedings (S. 376–384). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Metzger, N., Hoffmann, L., Bartelt, C., Stuckenschmidt, H., Wommer, M. und Bescos del Castillo, M. B. (2021). Towards trace-graphs for data-driven test case mining in the domain of automated driving. In , Third IEEE International Conference on Artificial Intelligence Testing: AITest 2021 : proceedings : 23–26 August 2021, online event (S. 41–48). , IEEE: Piscataway, NJ.
Nolte, F., Wilken, N. und Bartelt, C. (2021). Rendezvous delivery: Utilizing autonomous electric vehicles to improve the efficiency of last mile parcel delivery in urban areas. In , 2021 IEEE PerCom Workshops : PerAwareCity 2021, 6th IEEE Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems, March 22–26, 2021 in Kassel, Germany (S. 148–153). , IEEE Computer Society: Piscataway, NJ.
Pernpeintner, M., Bartelt, C. und Stuckenschmidt, H. (2021). Governing black-box agents in competitive multi-agent systems. In , Multi-Agent Systems : 18th European Conference, EUMAS 2021, virtual event, June 28–29, 2021, revised selected papers (S. 19–36). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Wilken, N., Stuckenschmidt, H. und Bartelt, C. (2021). Combining symbolic and data-driven methods for goal recognition. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 428–429). , IEEE Computer Society: Piscataway, NJ.
Burzlaff, F., Bongarth, B., Grottker, S., Hammen, J. und Bartelt, C. (2020). MergePoint: A graphical web-app for merging HTTP-endpoints and IoT-platform models. In , 53rd Hawaii International Conference on System Sciences, HICSS 2020 : Maui, Hawaii, USA, January 7–10, 2020 (S. 1–10). Proceedings of the 53rd Hawaii International Conference on System Sciences, ScolarSpace: Honolulu, HI.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2020). GUI2WiRe: Rapid wireframing with a mined and large-scale GUI repository using natural language requirements. In , ASE 2020 : 35th IEEE/ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Tool Demonstrations (S. 1297-1301). , ACM: New York, NY.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Robust decision tree induction from unreliable data sources. In , STAIRS 2020 : Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence (ECAI 2020) Santiago Compostela, Spain, August, 2020 (S. Paper 6, 1–8). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Schreckenberger, C., Glockner, T., Stuckenschmidt, H. und Bartelt, C. (2020). Restructuring of Hoeffding trees for Trapezoidal Data Streams. In , 20th IEEE International Conference on Data Mining Workshops : 17–20 November 2020, Virtual Conference : Proceedings (S. 416–423). , IEEE: Los Alamitos, CA [u. a.].
Burzlaff, F., Ackel, M. und Bartelt, C. (2019). A mapping language for IoT device descriptions. In , 2019 IEEE 43rd Annual Computer Software and Applications Conference : 15–19 July 2019, Milwaukee, Wisconsin : proceedings (S. 115–120). , IEEE Computer Society: Piscataway, NJ.
Burzlaff, F. und Bartelt, C. (2019). A conceptual architecture for enabling future self-adaptive service systems. In , 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, January 8–11, 2019 (S. 1–10). , AISeL: Atlanta, GA.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2019). Enhancing a crowd-based delivery network with mobility predictions. In , PredictGIS'19 : Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility : Chicago, IL, USA, November 05, 2019 (S. 66–75). Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, ACM: New York, NY.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2019). iDropout: Leveraging deep taylor decomposition for the robustness of deep neural networks. In , On the Move to Meaningful Internet Systems: OTM 2019 Conferences : Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings (S. 113–126). Lecture Notes in Computer Science, Springer: Berlin [u. a.].
Schreckenberger, C., Beckmann, S. und Bartelt, C. (2019). Next place prediction: A systematic literature review. In , PredictGIS 2018 : Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility : ACM GIS 2018 Conference: November 6 – November 9, 2018, Seattle, Washington (S. 37–45). Proceedings of the 2Nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility, ACM: New York, NY.
Burzlaff, F. und Bartelt, C. (2018). I4.0-device integration: A qualitative analysis of methods and technologies utilized by system integrators: Implications for enginering future industrial internet of things system. In , 2018 IEEE 15th International Conference on Software Architecture companion : ICSA-C 2018 : proceedings : 30 April-4 May 2018, Seattle, Washington (S. 27–34). , IEEE: Piscataway, NJ.
Burzlaff, F., Bartelt, C. und Adler, L. (2018). Towards automating service matching for manufacturing systems: Exemplifying knowledge-driven architecture composition. In , 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), Stockholm, Sweden, 16–18 May 2018 (S. 707–713). Procedia CIRP, Elsevier ; Curran: Amsterdam ; Red Hook, NY.
Burzlaff, F., Bartelt, C. und Jacobs, S. (2018). Executing model-based software development for embedded I4.0 devices properly. In , MOD-WS 2018 : Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018 (S. 35–46). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F., Bartelt, C. und Stuckenschmidt, H. (2018). Next steps in knowledge-driven architecture composition. In , LWDA 2018 : Proceedings of the Conference „Lernen, Wissen, Daten, Analysen“ Mannheim, Germany, August 22–24, 2018 (S. 78–83). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F. und Bartelt, C. (2017). Knowledge-driven architecture composition: Case-based formalization of integration knowledge to enable automated component coupling. In , ICSA 2017 : 2017 IEEE International Conference on Software Architecture : side track proceedings : proceedings : 3–7 April 2017, Gothenburg, Sweden (S. 108–111). , IEEE: Piscataway, NJ.

Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. The 38th Annual AAAI Conference on Artificial Intelligence, Vancouver, Canada.
Marton, S., Bartelt, C. und Stuckenschmidt, H. (2020). Machine learning for converting Black-Box models to interpretable functions. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Tree-based learning for dynamic data streams. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.

Andere Publikationen

[Standard] V-Modell®XT – Vorgehensmodell für die Durchführung von IT-Projekten, insb. zur Entwicklung von Softwaresystemen