Data Science Chair

    Julian Tritscher, M.Sc.

    Email: tritscher[at]

    Projects and Research Interests

    Explainable Artificial Intelligence  -  Anomaly Detection  -  Machine Learning

    I worked at the Data Science Chair from 2019 to 2024. I focused on investigating the explainable detection of anomalous and fraudulent behavior in ERP (Enterprise Resource Planning) systems in the DeepScan project. Further, I worked on the AI@Knauf project, integrating machine learning approaches into industrial manufacturing processes.

    The research focus of my dissertation was the domain of explainable artificial intelligence (XAI). Here I worked on providing intuitive explanations for decisions from complex anomaly detection approaches.


    • Fundamentals of Algorithms and Data Structures (winter term 2020/21, winter term 2021/22, winter term 2022/23, winter term 2023/24)
    • Data Mining (summer term 2019, summer term 2020)
    • Seminar "Selected Chapters from Machine Learning" (summer term 2019)


    2024[ to top ]
    • Generative Inpainting for...
      Tritscher, J., Lissmann, P., Wolf, M., Krause, A., Hotho, A., and Schlör, D. (2024) Generative Inpainting for Shapley-Value-Based Anomaly Explanation, The World Conference on eXplainable Artificial Intelligence (xAI 2024) - to appear.
    • Data Generation for Expla...
      Tritscher, J., Wolf, M., Krause, A., Hotho, A., and Schlör, D. (2024) Data Generation for Explainable Occupational Fraud Detection, 47th German Conference on Artificial Intelligence (KI 2024) - to appear.
    2023[ to top ]
    • Feature relevance XAI in ...
      Tritscher, J., Krause, A., and Hotho, A. (2023) Feature relevance XAI in anomaly detection: Reviewing approaches and challenges, Frontiers in Artificial Intelligence, 6, available:
    • Evaluating feature releva...
      Tritscher, J., Wolf, M., Hotho, A., and Schlör, D. (2023) Evaluating feature relevance XAI in network intrusion detection, The World Conference on eXplainable Artificial Intelligence (xAI 2023).
    • Occupational Fraud Detect...
      Tritscher, J., Roos, A., Schlör, D., Hotho, A., and Krause, A. (2023) Occupational Fraud Detection through Agent-based Data Generation, The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023.
    2022[ to top ]
    • Open ERP System Data For ...
      Tritscher, J., Gwinner, F., Schlör, D., Krause, A., and Hotho, A. (2022) Open ERP System Data For Occupational Fraud Detection, arxiv, available:
    • Towards Explainable Occup...
      Tritscher, J., Schlör, D., Gwinner, F., Krause, A., and Hotho, A. (2022) Towards Explainable Occupational Fraud Detection, Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Communications in Computer and Information Science(1753), 79–96, available:
    2021[ to top ]
    • A financial game with opp...
      Tritscher, J., Krause, A., Schlör, D., Gwinner, F., Von Mammen, S., and Hotho, A. (2021) A financial game with opportunities for fraud, in 2021 IEEE Conference on Games (CoG), 1–5, available:
    2020[ to top ]
    • Emote-Controlled: Obtaini...
      Kobs, K., Zehe, A., Bernstetter, A., Chibane, J., Pfister, J., Tritscher, J., and Hotho, A. (2020) Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Channels, ACM Transactions on Social Computing, available:
    • Evaluation of Post-hoc XA...
      Tritscher, J., Ring, M., Schlör, D., Hettinger, L., and Hotho, A. (2020) Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data, in Helic, D., Leitner, G., Stettinger, M., Felfernig, A. and Raś, Z.W., eds., Foundations of Intelligent Systems, Cham: Springer International Publishing, 422–430.