My name is Lea Cohausz and I’m a PhD student specializing in Artificial Intelligence at the University of Mannheim. I am notoriously interested in many things which is reflected in pretty much every aspect of my life.

Research Interests

  • causal modelling and causal structure learning
  • causal modelling and fairness
  • Explainability
  • combining causal inference and predictive models
  • much of the above in the context of Educational Data Mining
  • (AI-Planning and Reinforcement Learning for Goal and Plan Recognition)

If you are interested in any of these topics, feel free to reach out to me.

Education

  • since 2021: PhD student at the University of Mannheim (Computer Science)
  • 2018-2020: M.Sc. Data Science, University of Mannheim
  • 2018-2021: M.A. Sociology, University of Mannheim

Scholarships

  • 2017-2021: Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation)
  • 2016-2017: Deutschlandstipendium

Teaching

I am actively involved in teaching several courses:

  • Decision Support: Master-level course on the basics of logic and probability theory, graphical models (in particular BNs), utility theory, game theory, and reinforcement learning
  • Industrial Applications of AI (my sessions: agriculture/computer vision and education/XAI/causal modelling and biases): hands-on Master-level course
  • Master Team Project and Master Seminar Content Recommendation

Workshops & Tutorials

I have (co-)organized workshops and tutorials:

  • EDM 2024: Tutorial on Thinking Causally in EDM: A Hands-On Tutorial for Causal Modeling Using DAGs
  • EDM 2024: Workshop on Human-Centric eXplainable AI in Education (HEXED) with Juan D. Pinto, Luc Paquette, Vinitra Swamy, Tanja Käser, and Qianhui Liu
  • DELFI 2024: Workshop on Learning Analytics: Study Path and Curriculum Analytics with Frederik Baucks and Niels Seidel

Interests Outside Academia

I enjoy being outside, going on hikes, bouldering, sailing, playing all kinds of sports, and being a volunteer firefighter.

Social Involvement

Publications

Lea Cohausz, Andrej Tschalzev, Christian Bartelt & Heiner Stuckenschmidt (2024). Investigating Demographic Features and their Connection to Performance, Predictions, and Fairness in EDM Models. Journal of Educational Data Mining, 16(2), 177-213. The paper

Lea Cohausz, Jakob Kappenberger & Heiner Stuckenschmidt (2024). Combining Fairness and Causal Graphs to Advance Both. Workshop Proceedings of the European Conference of Artificical Intelligence 2024. Workshop on Fairness and Bias in AI. The paper

Nils Wilken, Lea Cohausz, Christian Bartelt, & Heiner Stuckenschmidt (2024). Fact Probability Vector Based Goal Recognition. Proceedings of the European Conference of Artificical Intelligence 2024, 392(4254-4261). The paper

Lea Cohausz, Frederik Baucks, & Niels Seidel (2024). Workshop Learning Analytics: Study Path and Curriculum Analytics. In Proceedings of DELFI Workshops 2024 (pp. 10-18420). Gesellschaft für Informatik eV.

Thilo I. Dieing, Marc Scheffler, & Lea Cohausz (2024). Enhancing Chatbot-Assisted Study Program Orientation. In Proceedings of DELFI Workshops 2024 (pp. 10-18420). Gesellschaft für Informatik eV.

Marc Scheffler, Thilo I. Dieing, & Lea Cohausz (2024). Developing a Personalized Study Program Recommender. In Proceedings of DELFI Workshops 2024 (pp. 10-18420). Gesellschaft für Informatik eV.

Lea Cohausz (2024). Thinking Causally in EDM: A Hands-On Tutorial for Causal Modeling Using DAGs.

Juan D. Pinto, Luc Paquette, Vinitra Swamy, Tanja Käser, Qianhui Liu & Lea Cohausz (2024). Human-Centric eXplainable AI in Education (HEXED) Workshop.

Lea Cohausz, Jakob Kappenberger & Heiner Stuckenschmidt. 2024. What fairness metrics can really tell you: A case study in the educational domain. Proceedings of the 14th International Conference on Learning Analytics and Knowledge. Download the Paper here

Lea Cohausz, Andrej Tschalzev, Christian Bartelt & Heiner Stuckenschmidt. 2023. Investigating the Importance of Demographic Features for EDM-Predictions. Proceedings of the 16th International Conference on Educational Data Mining. Download the Paper here Best Student Full Paper Award

Lea Cohausz. 2022. When Probabilities Are Not Enough - A Framework for Causal Explanations of Student Success Models. Journal of Educational Data Mining, 14(3), 52–75. Download the Paper here

Lea Cohausz. 2022. Towards Real Interpretability of Student Success Prediction Combining Methods of XAI and Social Science. Proceedings of the 15th International Conference on Educational Data Mining, 361–367. Download the Paper here Best Student Short Paper Award

Lea Cohausz, Nils Wilken & Heiner Stuckenschmidt. 2022. Plan-Similarity Based Heuristics for Goal Recognition. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 316-321). IEEE.

Nils Wilken, Lea Cohausz, Christian Batelt & Heiner Stuckenschmidt. 2023. Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?. arXiv preprint arXiv:2306.15362.

Sarah Alturki, Lea Cohausz & Heiner Stuckenschmidt. 2022. Predicting Master’s students’ academic performance: an empirical study in Germany. Smart Learning Environments, 9(1), 1-22. https://doi.org/10.1186/s40561-022-00220-y

Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan Lüdtke, Christian Bartelt and Heiner Stuckenschmidt. 2022. Leveraging Planning Landmarks for Hybrid Online Goal Recognition. ICAPS SPARK.