Tu Graz

Benedikt Joachim Kantz

Dipl.-Ing. BSc

Benedikt Kantz is a project assistant and PhD student within the HEREDITARY project since November 2024. He investigates the intersection between visualization, natural language processing, and information retrieval. Further research interests include geospatial visualization, eXplainable Artificial Intelligence (XAI), and Uncertainty Estimation.

He received a Bachelors degree in Information and Computer Engineering and a Masters degree in Computer Science at the Graz University of Technology.

benedikt.kantz@tugraz.at
+43 316 873 - 5408
dakantz.at
TUGRAZonline
Team Schreck
Benedikt Joachim Kantz

Publications

KnitSim: Knitting Simulation for Fabric Pattern Visualization
Kantz, B., Waldert, P., Schreck, T., Preiner, R.
18th International Symposium on Visual Information Communication and Interaction, VINCI 2025, 2025
Visual Exploration of Ontologies Supported by Language Models and Interactive Lenses
Kantz, B., Waldert, P., Lengauer, S., Schreck, T.
18th International Symposium on Visual Information Communication and Interaction, VINCI 2025, 2025
Privacy-Preserving Visual Exploration of Healthcare Data with a Language Model Integration
Grabner, M., Waldert, P., Kantz, B., Schreck, T.
Joint Proceedings of the ECAI 2025 Workshops XAI-KRKG and UCEX-XAI, Bologna, Italy, 2025
OnSET: Ontology and Semantic Exploration Toolkit
Kantz, B., Innerebner, K., Waldert, P., Lengauer, S., Lex, E., Schreck, T.
SIGIR 2025 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025, 3980-3984
Input Uncertainty Attribution by Uncertainty Propagation
Kantz, B., Steger, S., Staudinger, C., Feilmayr, C., Wachlmayr, J., Haberl, A., Schuster, S., Pernkopf, F.
ICASSP, 2025
Clusters in Focus: A Simple and Robust Detail-On-Demand Dashboard for Patient Data
Schilcher, L., Waldert, P., Kantz, B., Schreck, T.
EG VCBM 2025 - Eurographics Workshop on Visual Computing for Biology and Medicine, Short Paper Proceedings, 2025, (Eurographics Workshop on Visual Computing for Biomedicine)
Constrained Linked Entity ANnotation using RAG (CLEANR)
Kantz, B., Lengauer, S., Waldert, P., Schreck, T.
CEUR Workshop Proceedings, Vol. 4038, 2025, 343-358
Robustness of Explainable Artificial Intelligence in Industrial Process Modelling
Kantz, B., Staudinger, C., Feilmayr, C., Wachlmayr, J., Haberl, A., Schuster, S., Pernkopf, F.
ICML'24 Workshop ML for Life and Material Science: From Theory to Industry Applications, 2024