Professorship for Information Systems, in particular Machine Learning
Organisational unit: Professoship
Main research areas
Research
We are interested in statistical machine learning with a focus on spatiotemporal problems, such as user navigation on the web, adaptive testing and adaptive learning environments, and the coordination of football players on the pitch. While we mainly focus on basic research, we also collaborate with selected partners in academia, sports and industry in different projects.
Teaching
Our teaching focuses on introductory/advanced machine learning and data mining as well as basic statistics. We regularly offer courses in the Management & Data Science Master and the Information Systems Bachelor programs. Exemplary courses comprise Deep Learning (Data Science), Statistics (Information Systems), and Machine Learning & Data Mining (Engineering).
- 2025
- E-pub ahead of print
Self-improvement for Computerized Adaptive Testing
Rudolph, Y., Neubauer, K. & Brefeld, U., 2026, Machine Learning and Knowledge Discovery in Databases - Research Track: European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings. Ribeiro, R. P., Pfahringer, B., Japkowicz, N., Larrañaga, P., Jorge, A. M., Soares, C., Abreu, P. H. & Gama, J. (eds.). Cham: Springer International Publishing, Vol. 2. p. 70-86 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence); vol. 16014).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Muses or Stereotypes? Identifying Historical Patterns of Sexism in a Corpus of Brazilian Lyrics
Lopes, J. N. D. S., Firmino, V. P. & Dos Reis, V. Q., 05.06.2025, In: Journal on Interactive Systems. 16, 1, p. 369-380 12 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
The promise and challenges of computer mouse trajectories in DMHIs – A feasibility study on pre-treatment dropout predictions
Zantvoort, K., Matthiesen, J., Bjurner, P., Bendix, M., Brefeld, U., Funk, B. & Kaldo, V., 06.2025, In: Internet Interventions. 40, 7 p., 100828.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Interactive sequential generative models for team sports
Fassmeyer, D., Cordes, M. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 15 p., 38.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Masked autoencoder for multiagent trajectories
Rudolph, Y. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 18 p., 44.Research output: Journal contributions › Journal articles › Research › peer-review
- Accepted/In press
Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items
Bengs, D., Brefeld, U., Kroehne, U. & Zehner, F., 2025, (Accepted/In press) In: Psychometrika.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Machine Learning and Data Mining for Sports Analytics: 11th International Workshop, MLSA 2024, Vilnius, Lithuania, September 9, 2024, Revised Selected Papers
Brefeld, U. (Editor), Davis, J. (Editor), Van Haaren, J. (Editor) & Zimmermann, A. (Editor), 2025, Cham: Springer Verlag. 119 p. (Communications in Computer and Information Science; vol. 2460)Research output: Books and anthologies › Conference proceedings › Research
- Published
Toward Learning Distributions of Distributions
Wohlstein, M. & Brefeld, U., 2025, In: Proceedings of Machine Learning Research. 265, p. 269-275 7 p.Research output: Journal contributions › Conference article in journal › Research › peer-review
- 2024
- Submitted
Studying properties of water data using manifold-aware anomaly detectors
Paulsen, T. & Brefeld, U., 23.04.2024, (Submitted) Design of Cyber-Secure Water Plants. Mathur, A., Zhou, J. & Raman, G. (eds.). MDPI AG, (Water).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
- Published
Masked Autoencoder Pretraining for Event Classification in Elite Soccer
Rudolph, Y. & Brefeld, U., 26.02.2024, Machine Learning and Data Mining for Sports Analytics: 10th International Workshop, MLSA 2023, Revised Selected Papers. Brefeld, U., Davis, J., Van Haaren, J. & Zimmermann, A. (eds.). Cham: Springer Nature Switzerland AG, p. 24-35 12 p. (Communications in Computer and Information Science; vol. 2035).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review