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).
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Principled Interpolation in Normalizing Flows
Fadel, S., Mair, S., da Silva Torres, R. & Brefeld, U., 09.2021, Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II. Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J. & Lozano, J. A. (eds.). Cham: Springer Nature AG, p. 116-131 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12976 LNAI).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Predicting the future performance of soccer players
Arndt, C. & Brefeld, U., 01.10.2016, In: Statistical Analysis and Data Mining. 9, 5, p. 373-382 10 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring
Pandarova, I., Schmidt, T., Hartig, J., Boubekki, A., Jones, R. D. & Brefeld, U., 15.08.2019, In: International Journal of Artificial Intelligence in Education. 29, 3, p. 342-367 26 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Personalized Transaction Kernels for Recommendation Using MCTS
Tavakol, M., Joppen, T., Brefeld, U. & Fürnkranz, J., 01.09.2019, KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. Benzmüller, C. & Stuckenschmidt, H. (eds.). Wiesbaden: Springer, p. 338-352 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11793 LNAI).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Optimization Analysis for an Uncovered Wagon Transportation with an Interactive Animated Simulation-Based Platform for Multidisciplinary Learning
Wohlstein, M. H., Zakharova, E., Block, B. M. & Mercorelli, P., 2023, Proceedings of the 15th International Conference on Computer Supported Education - Volume 2, CSEDU 2023. Jovanovic, J., Chounta, I.-A., Uhomoibhi, J. & McLaren, B. (eds.). Science and Technology Publications, Lda (SciTePress), p. 451-457 7 p. (International Conference on Computer Supported Education, CSEDU - Proceedings; vol. 2).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Nmap: A novel neighborhood preservation space-filling algorithm
Duarte, F. S. L. G., Sikansi, F., Fatore, F. M., Fadel, S. G. & Paulovich, F. V., 31.12.2014, In: IEEE Transactions on Visualization and Computer Graphics. 20, 12, p. 2063-2071 9 p., 6876012.Research output: Journal contributions › Conference article in journal › Research › peer-review
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Modeling Conditional Dependencies in Multiagent Trajectories
Rudolph, Y. & Brefeld, U., 2022, In: Proceedings of Machine Learning Research. 151, p. 10518-10533 16 p.Research output: Journal contributions › Conference article in journal › Research › peer-review
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MDP-based itinerary recommendation using geo-tagged social media
Gaonkar, R., Tavakol, M. & Brefeld, U., 25.10.2018, Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, Proceedings. Duivesteijn, W., Siebes, A. & Ukkonen, A. (eds.). Basel: Springer Nature AG, p. 111-123 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); no. 11191).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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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
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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