Data Science Chair

    Julian Tritscher, M.Sc.

    Chair of Data Science (Informatik X)
    University of Würzburg
    Am Hubland
    97074 Würzburg

    Email: tritscher@informatik.uni-wuerzburg.de

    Phone: (+49 931)  31 - 84467

    Office: Room 50.03.016 (Institutsgebäude Künstliche Intelligenz)

    Projects and Research Interests

    Explainable Artificial Intelligence  -  Anomaly Detection  -  Machine Learning

    I am part of the Data Science Chair since I recieved my Master's Degree in Computer Science from the University of Würzburg in early 2019. I have worked in the past on investigating the explainable detection of anomalous and fraudulent behavior in ERP (Enterprise Resource Planning) systems in the DeepScan project. As part of the AI@Knauf project, I am currently working on integrating machine learning approaches into industrial manufacturing processes.

    My research focus is the domain of explainable artificial intelligence (XAI). Here I work 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)


    • PC member for the 8th Workshop on MIning DAta for financial applicationS (MIDAS 2023)


    2023[ to top ]
    • Evaluating feature releva...
      Tritscher, J., Wolf, M., Hotho, A., Schlör, D. (2023) Evaluating feature relevance XAI in network intrusion detection, The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear.
    • Occupational Fraud Detect...
      Tritscher, J., Roos, A., Schlör, D., Hotho, A., Krause, A. (2023) Occupational Fraud Detection through Agent-based Data Generation, The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear.
    • Feature relevance XAI in ...
      Tritscher, J., Krause, A., Hotho, A. (2023) Feature relevance XAI in anomaly detection: Reviewing approaches and challenges, Frontiers in Artificial Intelligence, 6, available: https://doi.org/10.3389/frai.2023.1099521.
    2022[ to top ]
    • Tritscher, J., Schlör, D., Gwinner, F., Krause, A., 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: https://doi.org/https://doi.org/10.1007/978-3-031-23633-4_7.
    • Open ERP System Data For ...
      Tritscher, J., Gwinner, F., Schlör, D., Krause, A., Hotho, A. (2022) Open ERP System Data For Occupational Fraud Detection, arxiv, available: https://arxiv.org/abs/2206.04460.
    2021[ to top ]
    • A financial game with opp...
      Tritscher, J., Krause, A., Schl{\"o}r, D., Gwinner, F., von Mammen, S., Hotho, A. (2021) A financial game with opportunities for fraud, IEE COG 2021, 2021, available: https://ieee-cog.org/2021/assets/papers/paper_273.pdf.
    2020[ to top ]
    • Emote-Controlled: Obtaini...
      Kobs, K., Zehe, A., Bernstetter, A., Chibane, J., Pfister, J., Tritscher, J., Hotho, A. (2020) Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels, ACM Transactions on Social Computing, available: https://doi.org/10.1145/3365523.
    • Evaluation of post-hoc XA...
      Tritscher, J., Ring, M., Schlör, D., Hettinger, L., Hotho, A. (2020) Evaluation of post-hoc XAI approaches through synthetic tabular data, International Symposium on Methodologies for Intelligent Systems.