The Data Science Chair (computer science institute JMU) is a highly interdisciplinary group of researchers led by Prof. Andreas Hotho and member of CAIDAS. We are active in data science and machine learning research with focus on deep learning and applications in multiple domains.
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University of Würzburg Data Science Chair (Computer Science X) Emil-Fischer-Straße 50 97074 Würzburg
BARTABSA++: Revisiting BARTABSA with Decoder LLMsJ. Pfister; T. Völker; A. Vlasjuk; A. Hotho H. Fei, K. Tu, Y. Zhang, X. Hu, W. Han, Z. Jia, Z. Zheng, Y. Cao, M. Zhang, W. Lu, N. Siddharth, L. Ovrelid, N. Xue, Y. Zhang (Eds.) (2025). 115–128.
SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-TranslationT. Völker; J. Pfister; A. Hotho S. Rosenthal, A. Rosá, D. Ghosh, M. Zampieri (Eds.) (2025). 852–864.
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from ScratchJ. Pfister; J. Wunderle; A. Hotho W. Che, J. Nabende, E. Shutova, M. T. Pilehvar (Eds.) (2025). 2227–2246.
CAIDAS at SemEval-2025 Task 7: Enriching Sparse Datasets with LLM-Generated Content for Improved Information RetrievalD. Benchert; S. Meßlinger; S. Goller; J. Kaiser; J. Pfister; A. Hotho S. Rosenthal, A. Rosá, D. Ghosh, M. Zampieri (Eds.) (2025). 1623–1638.
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionE. Fischer; A. Zehe; A. Hotho; D. Schlör in ACM Trans. Recomm. Syst. (2025).
Enriching Large Language Models with Knowledge Graphs for Computational Literary StudiesT. Hagen; J. Omeliyanenko; A. Ehrmanntraut; A. Hotho; A. Zehe; F. Jannidis in Handbook on Neurosymbolic AI and Knowledge Graphs (2025).
PreAdapter: Pre-training Language Models on Knowledge GraphsJ. Omeliyanenko; A. Hotho; D. Schlör G. Demartini, K. Hose, M. Acosta, M. Palmonari, G. Cheng, H. Skaf-Molli, N. Ferranti, D. Hernández, A. Hogan (Eds.) (2025). 210–226.
Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer NetworksS. Wankerl; J. Pfister; A. Dulny; G. Götz; A. Hotho in Transactions on Machine Learning Research (2025).
Adapting Sequential Recommender Models to Content Recommendation in Chat Data using Non-Item Page-ModelsA. Zehe; E. Fischer; J. Kaiser; T. Wagner; A. Hotho (2024).
Bayesian Inference for Composition of Hypotheses in Sequential Data. Technical Report (Master thesis), M. J. Levermann PhD thesis, Julius-Maximilians-Universität Würzburg, Professur für Inverse Probleme. (2024, September 28).
OtterlyObsessedWithSemantics at SemEval-2024 Task 4: Developing a Hierarchical Multi-Label Classification Head for Large Language ModelsJ. Wunderle; J. Schubert; A. Cacciatore; A. Zehe; J. Pfister; A. Hotho A. K. Ojha, A. S. Dougruöz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Rosá (Eds.) (2024). 602–612.
Pollice Verso at SemEval-2024 Task 6: The Roman Empire Strikes BackK. Kobs; J. Pfister; A. Hotho A. K. Ojha, A. S. Dougruöz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Rosá (Eds.) (2024). 1529–1536.
From Chat to Publication Management: Organizing your related work using BibSonomy & LLMsT. Völker; J. Pfister; T. Koopmann; A. Hotho in CHIIR ’24 (2024). 386–390.
Generative Inpainting for Shapley-Value-Based Anomaly ExplanationJ. Tritscher; P. Lissmann; M. Wolf; A. Krause; A. Hotho; D. Schlör in The World Conference on eXplainable Artificial Intelligence (xAI 2024) (2024).
We're excited to share that our paper "On the Role of Embeddings in Diffusion-based Generation of scRNA-seq Data", has been accepted at the workshop New Frontiers in Mining Complex Patterns at ECML PKDD 2025
