Data Science Chair

About Us

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.

Address

University of Würzburg
Data Science Chair (Computer Science X)
Emil-Fischer-Straße 50
97074 Würzburg

Phone.: +49 931 31-89675
sekretariat.lsx@uni-wuerzburg.de

We are part of:

Recent Publications

Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction. Fischer, Elisabeth; Zehe, Albin; Hotho, Andreas; Schlör, Daniel. In ACM Trans. Recomm. Syst. Association for Computing Machinery, New York, NY, USA, 2025
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PreAdapter: Pre-training Language Models on Knowledge Graphs. Omeliyanenko, Janna; Hotho, Andreas; Schl{ö}r, Daniel. In The Semantic Web -- ISWC 2024, G. Demartini, K. Hose, M. Acosta, M. Palmonari, G. Cheng, H. Skaf-Molli, N. Ferranti, D. Hern{á}ndez, A. Hogan (eds.), pp. 210–226. Springer Nature Switzerland, Cham, 2025
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Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer Networks. Wankerl, Sebastian; Pfister, Jan; Dulny, Andrzej; G{ö}tz, Gerhard; Hotho, Andreas. In Transactions on Machine Learning Research. 2025
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Exploring Design Choices for Autoregressive Deep Learning Climate Models. Gallusser, Florian; Hentschel, Simon; Krause, Anna; Hotho, Andreas. In Tackling Climate Change With Machine Learning, International Conference on Learning Representation 2025 (ICLR 2025). 2025
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Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction. Fischer, Elisabeth; Schlör, Daniel; Zehe, Albin; Hotho, Andreas. 2024, October
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Adapting Sequential Recommender Models to Content Recommendation in Chat Data using Non-Item Page-Models. Zehe, Albin; Fischer, Elisabeth; Kaiser, Jonas; Wagner, Toni; Hotho, Andreas. In Proceedings of the Sixth Knowledge-aware and Conversational Recommender Systems Workshop. 2024
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Bayesian Inference for Composition of Hypotheses in Sequential Data. Technical Report (Master thesis), . Levermann, Max Johann. Master thesis. Julius-Maximilians-Universität Würzburg, Professur für Inverse Probleme, 2024, September 28
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{O}tterly{O}bsessed{W}ith{S}emantics at {S}em{E}val-2024 Task 4: Developing a Hierarchical Multi-Label Classification Head for Large Language Models. Wunderle, Julia; Schubert, Julian; Cacciatore, Antonella; Zehe, Albin; Pfister, Jan; Hotho, Andreas. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), A. K. Ojha, A. S. Do{\u{g}}ru{ö}z, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Ros{á} (eds.), pp. 602–612. Association for Computational Linguistics, Mexico City, Mexico, 2024
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{S}uper{GLEB}er: {G}erman Language Understanding Evaluation Benchmark. Pfister, Jan; Hotho, Andreas. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), K. Duh, H. Gomez, S. Bethard (eds.), pp. 7904–7923. Association for Computational Linguistics, Mexico City, Mexico, 2024
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Pollice Verso at SemEval-2024 Task 6: The Roman Empire Strikes Back. Kobs, Konstantin; Pfister, Jan; Hotho, Andreas. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), A. K. Ojha, A. S. Do{\u{g}}ruöz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Rosá (eds.), pp. 1529–1536. Association for Computational Linguistics, Mexico City, Mexico, 2024
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From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs. Völker, Tom; Pfister, Jan; Koopmann, Tobias; Hotho, Andreas. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, of CHIIR ’24, pp. 386–390. Association for Computing Machinery, <conf-loc>, <city>Sheffield</city>, <country>United Kingdom</country>, </conf-loc>, 2024
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Generative Inpainting for Shapley-Value-Based Anomaly Explanation. Tritscher, Julian; Lissmann, Philip; Wolf, Maximilian; Krause, Anna; Hotho, Andreas; Schlör, Daniel. In The World Conference on eXplainable Artificial Intelligence (xAI 2024) - to appear. 2024
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Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale. Seufert, Michael; Dietz, Katharina; Wehner, Nikolas; Geißler, Stefan; Schüler, Joshua; Wolz, Manuel; Hotho, Andreas; Casas, Pedro; Hoßfeld, Tobias; Feldmann, Anja. In IEEE Transactions on Network and Service Management, pp. 1–1. 2024
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CompTrails: comparing hypotheses across behavioral networks. Koopmann, Tobias; Becker, Martin; Lemmerich, Florian; Hotho, Andreas. In Data Mining and Knowledge Discovery. 2024
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Global Vegetation Modeling with Pre-Trained Weather Transformers. Janetzky, Pascal; Gallusser, Florian; Hentschel, Simon; Hotho, Andreas; Krause, Anna. 2024
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Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction. Fischer, Elisabeth; Schl{{ö}}r, Daniel; Zehe, Albin; Hotho, Andreas. In CoRR, Vol. abs/2408.15953. 2024
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Digital Stylistics in Romance Studies and Beyond. Hesselbach, Robert; Calvo Tello, Jos{é}; Henny-Krahmer, Ulrike; Schöch, Christof; Schlör, Daniel. Heidelberg University Publishing (heiUP), 2024
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ModeConv: {A} Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior. Schaller, Melanie; Schl{{ö}}r, Daniel; Hotho, Andreas. In CoRR, Vol. abs/2407.00140. 2024
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GrINd: Grid Interpolation Network for Scattered Observations. Dulny, Andrzej; Heinisch, Paul; Hotho, Andreas; Krause, Anna. In Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, {ECML} {PKDD} 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part {VII}, Vol. 14947 of Lecture Notes in Computer Science, A. Bifet, J. Davis, T. Krilavicius, M. Kull, E. Ntoutsi, I. Zliobaite (eds.), pp. 177–193. Springer, 2024
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Verantwortungsvolle Empfehlungssysteme f{ü}r die medizinische Diagnostik. Schl{ö}r, Daniel; Hotho, Andreas. In Edition Moderne Postmoderne, p. 101. 2024
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News

Sprachmodelle: Deutschlands Aufholjagd gegenüber den USA und China

Im Interview mit heise.de spricht Prof. Dr. Andreas Hotho über die Herausforderungen bei der Entwicklung deutscher Sprachmodelle und stellt das komplett auf deutschen Daten trainierte Sprachmodell LLämMlein vor.

Mehr

Our paper „Exploring Design Choices for Autoregressive Deep Learning Climate Models“ has been accepted at the ICLR25 TCCML Workshop

We’re excited to share that our paper, “Exploring Design Choices for Autoregressive Deep Learning Climate Models,” has been accepted to the Tackling Climate Change with Machine Learning (TCCML) Workshop at ICLR 2025!

Mehr

Data Science Chair

About Us

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We are part of:

Recent Publications

News

Sprachmodelle: Deutschlands Aufholjagd gegenüber den USA und China

Our paper „Exploring Design Choices for Autoregressive Deep Learning Climate Models“ has been accepted at the ICLR25 TCCML Workshop

SemEval'25 Task 2 & Task 7 Participation - First Place for Task 2!

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