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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Space and Control in Soccer
Martens, F., Dick, U. & Brefeld, U., 16.07.2021, In: Frontiers in Sports and Active Living . 3, 13 p., 676179.Research output: Journal contributions › Journal articles › Research
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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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Contextual movement models based on normalizing flows
Fadel, S., Mair, S., da Silva Torres, R. & Brefeld, U., 03.2023, In: AStA Advances in Statistical Analysis. 107, 1-2, p. 51-72 22 p.Research output: Journal contributions › Journal articles › Research › peer-review
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The origins of goals in the German Bundesliga
Anzer, G., Bauer, P. & Brefeld, U., 17.11.2021, In: Journal of Sports Sciences. 39, 22, p. 2525-2544 20 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Joint optimization of an autoencoder for clustering and embedding
Boubekki, A., Kampffmeyer, M., Brefeld, U. & Jenssen, R., 01.07.2021, In: Machine Learning. 110, 7, p. 1901-1937 37 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Analysing Positional Data
Brefeld, U., Mair, S. & Lasek, J., 01.10.2020, Science Meets Sports: When Statistics Are More Than Numbers. Ley, C. & Dominicy, Y. (eds.). Newcastle upon Tyne: Cambridge Scholars Publishing, p. 81-94 13 p. (Physical Sciences).Research output: Contributions to collected editions/works › Chapter › peer-review
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Uma Caracterização das Polı́ticas de Privacidade Utilizadas em Aplicativos no Brasil
Jardim, G. P. S., Rabello, M. E. R., Lima, A. C., Brefeld, U. & Quadros dos Reis, V., 05.08.2022, Anais do III Workshop sobre as Implicações da Computação na Sociedade (WICS). Sociedade Brasileira de Computação (SBC), p. 13-25 13 p. (WORKSHOP SOBRE AS IMPLICAÇÕES DA COMPUTAÇÃO NA SOCIEDADE (WICS); no. 3/2022).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Semi-Supervised Generative Models for Multi-Agent Trajectories
Brefeld, U., Fassmeyer, D. & Fassmeyer, P., 2022, Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Red Hook: Curran Associates, Vol. 48. p. 37267-37281 15 p. (Advances in Neural Information Processing Systems; vol. 35).Research output: Contributions to collected editions/works › Article in conference proceedings › 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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Graph Conditional Variational Models: Too Complex for Multiagent Trajectories?
Rudolph, Y., Brefeld, U. & Dick, U., 2020, In: Proceedings of Machine Learning Research. 137, p. 136-147 12 p.Research output: Journal contributions › Conference article in journal › Research › peer-review