piwik-script

Intern
    Data Science Chair

    Julian Tritscher, M.Sc.

    Chair of Data Science (Informatik X)
    University of Würzburg
    Campus Hubland Nord
    Emil-Fischer-Straße 50
    97074 Würzburg
    Germany

    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.

    Teaching

    • 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)

    Activities

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

    Publications

    2023[ to top ]
    • 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) - to appear.
    • 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: https://doi.org/10.3389/frai.2023.1099521.
    • 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 - to appear.
    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: https://arxiv.org/abs/2206.04460.
    • 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: https://doi.org/https://doi.org/10.1007/978-3-031-23633-4_7.
    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: https://doi.org/10.1109/CoG52621.2021.9619070.
    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 Twitch.tv Channels, ACM Transactions on Social Computing, available: https://doi.org/10.1145/3365523.
    • Evaluation of Post-hoc XA...
      Tritscher, J., Ring, M., Schlr, 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 Ra{\’{s}}, Z.W., eds., Foundations of Intelligent Systems, Cham: Springer International Publishing, 422–430.