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

    Linking Up: Recommendation of Co-Authorship using Hypergraph Networks

    Are you a budding scientist searching for a topic to delve into? Do you possess a sharp mind and a hunger for knowledge? Then, look no further! Our esteemed institution is searching for candidates to undertake a thesis that utilizes hypergraph neural networks to prognosticate co-authorships between scientific authors. Your work may revolutionize scientific collaboration as we know it! Apply now and join the ranks of the elite few who can claim to have contributed to the progress of science! And don't forget to thank me in your thesis acknowledgement section.

    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


    Weights Watchers: Using Deep Learning to Fine-tune HypTrails Hypothesis Mixtures

    We require bright, ambitious students who are willing to undertake the task of developing a revolutionary method for optimizing the mixtures of hypotheses explaining human navigation and behavior. And, no slacking off. We expect you to use the latest and greatest techniques in deep learning to accomplish this task.

    Your goal is to tackle the challenge of tuning the weighted combination of hypotheses, which are used as a prior to a Bayesian framework called HypTrails. If successful, you'll contribute to an exciting new era of human behavior research.

    But, let's not forget the most important thing - you must be willing to face the challenges head-on and never give up. Remember, failure is not an option, and I won't tolerate any half-hearted efforts.

    Options: Bachelor Thesis, Master Praktika


    From Ancient to modern: Tackling the big scary world of database migration

    Greetings prospective students, do you have a passion for database updates? Well then, have I got a project for you! We need to modernize our ancient MySQL database powering the Website Bibsonomy, which contains over 200 GB of data, by updating it to something not that ancient, like MariaDB or Postgres DB. Your task will be to change the queries from the current backend to the new databases, optimise them to make them more efficient and thundering fast. This will be a challenging project, but don't worry, we will be right here to provide guidance and support. So, gear up and embark on this exciting adventure with the promise of a delicious cake upon completion. Remember, the cake is a lie only if you don't put in the effort.

    Options: Bachelor Thesis, Master Praktika


    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.