Julian Tritscher, M.Sc.
Chair of Data Science (Informatik X)
University of Würzburg
Phone: (+49 931) 31 - 84467
Office: Room B109 (Computer Science Building M2)
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.
- Open Bachelor/Master Thesis: I have several possible thesis topics available here
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)
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)
Open ERP System Data For Occupational Fraud Detection, arxiv, available: https://arxiv.org/abs/2206.04460.(2022)
A financial game with opportunities for fraud, IEE COG 2021, 2021, available: https://ieee-cog.org/2021/assets/papers/paper_273.pdf.(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, International Symposium on Methodologies for Intelligent Systems.(2020)