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[at]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
Activities
- PC member for the 8th Workshop on MIning DAta for financial applicationS (MIDAS 2023)
Publications
-
“Generative Inpainting for Shapley-Value-Based Anomaly Explanation”, The World Conference on eXplainable Artificial Intelligence (xAI 2024) - to appear.(2024)
-
“Feature relevance XAI in anomaly detection: Reviewing approaches and challenges”, Frontiers in Artificial Intelligence, 6, available: https://doi.org/10.3389/frai.2023.1099521.(2023)
-
“Evaluating feature relevance XAI in network intrusion detection”, The World Conference on eXplainable Artificial Intelligence (xAI 2023).(2023)
-
“Occupational Fraud Detection through Agent-based Data Generation”, The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023.(2023)
-
“Open ERP System Data For Occupational Fraud Detection”, arxiv, available: https://arxiv.org/abs/2206.04460.(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.(2022)
-
“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.(2021)
-
“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.(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.(2020)