Deutsch Intern
    Data Science Chair

    Tobias Koopmann, M.Sc.

    Chair of Data Science (Informatik X)
    University of Würzburg
    Am Hubland
    97074 Würzburg

    Email: koopmann@informatik.uni-wuerzburg.de

    Phone: (+49 931)  31 - 89363

    Office: Room B100 (Computer Science Building M2)

    Fingerprint:  CBC7 EACE BBF3 8CF8 EB3F 3478 A5FB FAA7 8F34 AD4E

    Projects and Research Interests

    I joined the DMIR group for my PhD studies after receiving my masters degree in Computer Science at the university of Würzburg in early 2019. At first, i was working on click trail analysis and human behavior prediction in the web based on Wikipedia click trails. 

    I started to work in the REGIO project. My contribution in the project is to analyse locally successful research cooperations based on different properties of the co-author network. For this we leverage the Bayesian approach HypTrails and apply it on graph- structured data. End 2021 after the project has been successfully finalized, I started working on the HydrAS project, which is a natural succession for my research topic. 

    Current Thesis/Practica Topics

    Recommendation of Co-Authorship using Hypergraph Networks

    Recently a new field of Graph Neural Networks has been revealed, which takles the field of Hypergraph networks. These networks use hyperedges, which can connect mutiple vertexes. A bibliometric dataset contains authors as vertices and co-occurences on publications as hyperedges. Given this dataset and the method of Hypergraph networks, we can now train a network to predict co-authorship.

    Feng, Y., You, H., Zhang, Z., Ji, R., & Gao, Y. (2019, July). Hypergraph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 3558-3565).

    Options: Ma Praktika, Ma Thesis


    Analysis of Transformer Networks with respect to Layer Dropout and Dataset Splits

    Recent work hast proposed the transformer architecture. Other work analysed this architecture regarding its sensitivity on dataset splits with respect to the dataset length.
    The main task of this work is to evaluate the transformer and/or BERT architecture with respect to dataset splits not only dependant of the length.  

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems30.

    Csordás, R., Irie, K., & Schmidhuber, J. (2021). The devil is in the detail: Simple tricks improve systematic generalization of transformers. arXiv preprint arXiv:2108.12284.

    Options: Ba Thesis, Ma Thesis


    Implementation of Automatic HypTrails Framework

    The goal of this work is to implement a Python framework, which automatically creates all possible hypothesis and calculates their respective evidences according to HypTrail[1]. The input for this framework is supposed to be an attributed multigraph[2] and some distance measures for each attribute of the nodes.

    Possible thesis: BA, MP, MA 

    [1] Singer, P., Helic, D., Hotho, A. & Strohmaier, M. (2015). HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web. Proceedings of the 24th International Conference on World Wide Web (p./pp. 1003--1013), New York, NY, USA: ACM. ISBN: 978-1-4503-3469-3 

    [2] Espín-Noboa, L., Lemmerich, F., Strohmaier, M. et al. JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs. Appl Netw Sci 2, 16 (2017). https://doi.org/10.1007/s41109-017-0036-1



    2021[ to top ]
    • CoBERT: Scientific Collab...
      Koopmann, T., Kobs, K., Herud, K., Hotho, A. (2021) CoBERT: Scientific Collaboration Prediction via Sequential Recommendation, in 2021 International Conference on Data Mining Workshops (ICDMW), 45–54, available: https://doi.org/10.1109/ICDMW53433.2021.00013.
    • Proximity dimensions and ...
      Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G. (2021) Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research, Scientometrics, available: https://doi.org/10.1007/s11192-021-03922-1.
    2020[ to top ]
    • Stubbemann, M., Koopmann, T. (2020) The German and International AI Network Data Set, available: https://doi.org/10.5281/zenodo.3693604.
    • Kobs, K., Koopmann, T., Zehe, A., Fernes, D., Krop, P., Hotho, A. (2020) Where to Submit Helping Researchers to Choose the Right Venue, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, Online: Association for Computational Linguistics, 878–883, available: https://www.aclweb.org/anthology/2020.findings-emnlp.78.
    2019[ to top ]
    • On the Right Track! Analy...
      Koopmann, T., Dallmann, A., Hettinger, L., Niebler, T., Hotho, A. (2019) On the Right Track! Analysing and Predicting Navigation Success in Wikipedia, in Proceedings of the 30th ACM Conference on Hypertext and Social Media, HT ’19, Hof, Germany: ACM, 143–152, available: https://doi.org/10.1145/3342220.3343650.