Isabelle Kuhlmann

Isabelle Kuhlmann Photo: private

Email: isabelle.kuhlmann

Phone: +49 2331 987-4020

Curriculum vitae

Academic Career

2021 – now Scientific Employee,FernUniversität in Hagen
2020 – 2021 Scientific Employee,University of Koblenz-Landau, Koblenz
2017 – 2020 M. Sc. Computational Visualistics,University of Koblenz-Landau, Koblenz
2013 – 2017 B. Sc. Computational Visualistics,University of Koblenz-Landau, Koblenz

Honors & Awards

2021 Hochschulpreis der Wirtschaft, Awarded for Master Thesis, 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 RoboCup World Cup 2019 Sydney,@home Open Platform League, 1st place
2019 RoboCup German Open 2019 Magdeburg,@home Open Platform League, 3rd place
2018 Hochschulpreis der Wirtschaft, Awarded for Bachelor Thesis, IHK Koblenz

Selected Publications

  • Carl Corea, Isabelle Kuhlmann, Matthias Thimm, John Grant. Paraconsistent Reasoning for Inconsistency Measurement in Declarative Process Specifications. In Information Systems, 122:102347. February 2024.
  • Isabelle Kuhlmann, Andreas Niskanen, Matti Järvisalo. Computing MUS-Based Inconsistency Measures. In Proceedings of the European Conference on Logics in Artificial Intelligence (JELIA'23). September 2023.
  • Andreas Niskanen, Isabelle Kuhlmann, Matthias Thimm, Matti Järvisalo. MaxSAT-Based Inconsistency Measurement. In Proceedings of the 26th European Conference on Artificial Intelligence (ECAI'23). September 2023.
  • Isabelle Kuhlmann, Carl Corea, John Grant. An ASP-Based Framework for Solving Problems Related to Declarative Process Specifications. In Proceedings of the 21st International Workshop on Non-Monotonic Reasoning (NMR'23). September 2023.
  • 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, 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.
  • Isabelle Kuhlmann, Tjitze Rienstra, Lars Bengel, Kenneth Skiba and Matthias Thimm. Distinguishability inAbstract Argumentation. Proceedings of the 18th International Conference on Principles ofKnowledge Representation and Reasoning (KR’21), 2021.
  • Isabelle Kuhlmann, Viktor Seib and Dietrich Paulus. Integrating Feedforward Design into a GenerativeNetwork to Synthesize Supplementary Training Data for Object Classification. In Proceedings of the 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC'21). April 2021.
  • Isabelle Kuhlmann, Matthias Thimm. An Algorithm for the Contension Inconsistency Measure using Reductions to Answer Set Programming. In Proceedings of the 14th International Conference on Scalable Uncertainty Management (SUM'20). September 2020. (Best Student Paper)
  • Isabelle Kuhlmann, Matthias Thimm. Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study. In Proceedings of the 13th International Conference on Scalable Uncertainty Management (SUM'19). December 2019.