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Professorship for Information Systems, in particular Machine Learning

Organisational unit: Professoship

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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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  1. Published

    HyperUCB: Hyperparameter optimization using contextual bandits

    Tavakol, M., Mair, S. & Morik, K., 28.03.2020, Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. Cellier, P. & Driessens, K. (eds.). Cham: Springer Nature AG, Vol. 1. p. 44-50 7 p. ( Communications in Computer and Information Science; vol. 1167).

    Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

  2. Published

    How Much Home Office is Ideal? A Multi-Perspective Algorithm

    Colley, M., Jansen, P., Matthiesen, J., Hoberg, H., Reger, C. & Thiermann, I., 20.09.2023, Proceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work, CHIWORK 2023. Boll, S., Cox, A., Ludwig, T. & Cecchinato, M. E. (eds.). New York: Association for Computing Machinery, Inc, 12 p. 8. (ACM International Conference Proceeding Series).

    Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

  3. Published

    Hands in Focus: Sign Language Recognition Via Top-Down Attention

    Sarhan, N., Wilms, C., Closius, V., Brefeld, U. & Frintrop, S., 08.10.2023, 2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings: Proceedings. Piscataway: IEEE Electromagnetic Compatibility Society, p. 2555-2559 5 p. (Proceedings - International Conference on Image Processing, ICIP).

    Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

  4. Published

    Guest editorial: Special issue on sports analytics

    Brefeld, U. & Zimmermann, A., 01.11.2017, In: Data Mining and Knowledge Discovery. 31, 6, p. 1577-1579 3 p.

    Research output: Journal contributions › Other (editorial matter etc.) › Research

  5. Published

    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

  6. Published

    Frame-based Optimal Design

    Mair, S., Rudolph, Y., Closius, V. & Brefeld, U., 23.01.2019, Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings. Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N. & Ifrim, G. (eds.). Cham: Springer Nature AG, Vol. 2. p. 447-463 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11052 LNAI).

    Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

  7. Published

    Frame-based Data Factorizations

    Mair, S., Boubekki, A. & Brefeld, U., 25.07.2017, 34th International Conference on Machine Learning, ICML 2017. Precup, D. & Teh, Y. W. (eds.). Red Hook: Curran Associates, p. 2305-2313 9 p. (Proceedings of Machine Learning Research; vol. 70).

    Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

  8. Published

    Foreword to applied data science, demo, and nectar tracks

    Brefeld, U., Curry, E., Daly, E., Macnamee, B., Marascu, A. & Pinelli, F., 01.01.2019, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings. Brefeld, U., Curry, E., Daly, E., Macnamee, B., Marascu, A. & Pinelli, F. (eds.). Cham: Springer, Vol. 3. p. V-VI 2 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11053).

    Research output: Contributions to collected editions/works › Other › Research

  9. Published

    Feature Extraction and Aggregation for Predicting the Euro 2016

    Tavakol, M., Zafartavanaelmi, H. & Brefeld, U., 09.2016, In: CEUR Workshop Proceedings. 1842, 1842, 7 p.

    Research output: Journal contributions › Conference article in journal › Research › peer-review

  10. Published

    FaST: A linear time stack trace alignment heuristic for crash report deduplication

    Rodrigues, I. M., Aloise, D. & Fernandes, E. R., 23.05.2022, The 2022 Mining Software Repositories Conference: MSR 2022, Proceedings; 18-20 May 2022, Virtual; 23-24 May 2022, Pittsburgh, Pennsylvania. New York: Institute of Electrical and Electronics Engineers Inc., p. 549-560 12 p. (Proceedings - IEEE/ACM International Conference on Mining Software Repositories ).

    Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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