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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

    A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations

    Tavakol, M. & Brefeld, U., 30.12.2017, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. Ceci, M., Hollmen, J., Todorovski, L., Vens, C. & Dzeroski, S. (eds.). Springer, Vol. 2. p. 269-284 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10535 LNAI).

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

  2. Published

    Data-driven analyses of electronic text books

    Boubekki, A., Kröhne, U., Goldhammer, F., Schreiber, W. & Brefeld, U., 2016, Solving large scale learning tasks: Challenges and algorithms : essays dedicated to Katharina Morik on the occasion of her 60th birthday. Michaelis, S., Piatkowski, N. & Stolpe, M. (eds.). Cham: Springer International Publishing AG, p. 362-376 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9580).

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

  3. 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

  4. Published

    Exploiting ConvNet diversity for flooding identification

    Nogueira, K., Fadel, S. G., Dourado, I. C., De Werneck, R. O., Munoz, J. A. V., Penatti, O. A. B., Calumby, R. T., Li, L. T., Dos Santos, J. A. & Torres, R. D. S., 09.2018, In: IEEE Geoscience and Remote Sensing Letters. 15, 9, p. 1446-1450 5 p., 8398414.

    Research output: Journal contributions › Journal articles › Research › peer-review

  5. Published

    Adaptive Item Selection Under Matroid Constraints

    Bengs, D., Brefeld, U. & Kröhne, U., 07.08.2018, In: Journal of Computerized Adaptive Testing. 6, 2, p. 15-36 22 p.

    Research output: Journal contributions › Journal articles › Research › peer-review

  6. Published

    Infinite Mixtures of Markov Chains

    Reubold, J., Boubekki, A., Strufe, T. & Brefeld, U., 2018, New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP 2017 : held in conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : revised selected papers. Appice, A., Loglisci, C., Manco, G. & Masciari, E. (eds.). Cham: Springer, p. 167-181 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10785 LNAI).

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

  7. Published

    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

  8. Published

    Probabilistic movement models and zones of control

    Brefeld, U., Lasek, J. & Mair, S., 15.01.2019, In: Machine Learning. 108, 1, p. 127-147 21 p.

    Research output: Journal contributions › Journal articles › Research › peer-review

  9. Published

    Coresets for Archetypal Analysis

    Mair, S. & Brefeld, U., 2020, 32rd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019. Wallach, H. & Larochelle, H. (eds.). Red Hook: Curran Associates, Vol. 10. p. 7215-7223 9 p. (Advances in neural information processing systems; vol. 32).

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

  10. Published

    Zur empirischen Beforschung des mBooks Belgien: Die Chancen eines Methodenmix

    Schreiber, W., Wagner, W., Trautwein, U. & Brefeld, U., 10.2019, Das Geschichtsschulbuch: Lehren – Lernen – Forschen . Kühberger, C., Bernhard, R. & Bramann, C. (eds.). Münster: Waxmann Verlag, p. 57-80 24 p. (Salzburger Beiträge zur Lehrer/innen/bildung; vol. 6).

    Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review

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