Isabelle Kuhlmann
Lebenslauf
Studium und Ausbildung
| 2017 – 2020 | M. Sc. Computervisualistik, Universität Koblenz-Landau, Koblenz |
| 2013 – 2017 | B. Sc. Computervisualistik, Universität Koblenz-Landau, Koblenz |
Akademischer Werdegang
| seit 2021 | Wissenschaftliche Mitarbeiterin, FernUniversität in Hagen |
| 2020 – 2021 | Wissenschaftliche Mitarbeiterin, Universität Koblenz-Landau, Koblenz |
Auszeichnungen
| 2021 | Hochschulpreis der Wirtschaft, Verliehen für die Masterarbeit, IHK Koblenz |
| 2020 | Best Student Paper Award, 14th Intl. Conf. on Scalable Uncertainty Management, 2020 |
| 2019 | Best Team Award Consumer League, European Robotics League, 2018-19 |
| 2019 | Best Team Award Professional League, European Robotics League, 2018-19 |
| 2019 | 1. Platz @home Open Platform League, RoboCup World Cup 2019 Sydney |
| 2019 | 3. Platz @home Open Platform League, RoboCup German Open 2019 Magdeburg |
| 2018 | Hochschulpreis der Wirtschaft,Verliehen für die Bachelorarbeit, IHK Koblenz |
Betreute Arbeiten
Bachelor-Arbeiten
- Bachelorarbeit: "Untersuchung des Einflusses von Graph-Eigenschaften auf die Vorhersagequalität maschineller Lernverfahren im Kontext abstrakter Argumentation", in Bearbeitung
- Bachelorarbeit: "Vorverarbeitungsmethoden für Inkonsistenzmessung", in Bearbeitung
Master-Arbeiten
- Masterarbeit: "Entwicklung von Algorithmen für Inkonsistenzmessung auf Basis von QBF-Solving und ganzzahliger linearer Optimierung", in Bearbeitung
Auswahl der 10 wichtigsten Publikationen
- Isabelle Kuhlmann, Anna Gessler, Vivien Laszlo, Matthias Thimm. A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure. In Proceedings of the 15th international conference on Scalable Uncertainty Management (SUM'22). October 2022.
- Isabelle Kuhlmann. On Bridging the Gap Between Machine Learning and Knowledge Representation and Reasoning: The Case of Abstract Argumentation. In Proceedings of the First International Conference on Foundations, Applications, and Theory of Inductive Logic (FATIL'22). October 2022.
- Isabelle Kuhlmann, Thorsten Wujek, Matthias Thimm. On the Impact of Data Selection when Applying Machine Learning in Abstract Argumentation. In Proceedings of the 9th International Conference on Computational Models of Argument (COMMA'22). September 2022.
- Kuhlmann, I. and Thimm, M. „Algorithms for Inconsistency Measurement using AnswerSet Programming.“Proceedings of the 19th International Workshop on Non-MonotonicReasoning (NMR’21), 2021
- Kuhlmann, I., Rienstra, T., Bengel, L., Skiba, K. and Thimm, M. „Distinguishability inAbstract Argumentation.“Proceedings of the 18th International Conference on Principles ofKnowledge Representation and Reasoning (KR’21), 2021.
- Kuhlmann, I. „Towards Eliciting Attacks in Abstract Argumentation Frameworks“, OnlineHandbook of Argumentation for AI, Volume 2, 2021
- Kuhlmann, I., Seib, V. and Paulus, D. „Integrating Feedforward Design into a GenerativeNetwork to Synthesize Supplementary Training Data for Object Classification“, 2021 IEEEInternational Conference on Autonomous Robot Systems and Competitions (ICARSC), IEEE 2021
- Kuhlmann, I. and Thimm, M. „An Algorithm for the Contension Inconsistency Measureusing Reductions to Answer Set Programming“, 14th International Conference on ScalableUncertainty Management.Springer, Cham, 2020 (Best Student Paper)
- Kuhlmann, I. and Thimm, M. „Using Graph Convolutional Networks for ApproximateReasoning with Abstract Argumentation Frameworks: A Feasibility Study“, 13th InternationalConference on Scalable Uncertainty Management, Springer, Cham, 2019
- Memmesheimer, R., Kuhlmann, I., Mints, M., Schmidt, P., Korbach, C., Germann, I. and Paulus, D. „Scratchy: a lightweight modular autonomous robot for robotic competitions“, 2019IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), IEEE, 2019
10.08.2022
