Data Science Chair

    Dr.-Ing. Anna Krause

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

    Email: anna.krause[at]informatik.uni-wuerzburg.de
    PGP-Key: Download(E013 7ACA 2DCF 8DAC 5E51 0406 0759 C72A B9A7 510A)

    Phone: (+49 931)  31 - 88935

    Office: Room B107 (Computer Science Building M2)

    About Me

    I received my Diploma in Electrical Engineering from the Technical University Dresden in 2009. In the same year, I joined Prof. Erich Barke's Electronic Design Automation Group at the Institute of Microelectronic Systems at the University of Hannover. I researched methods for automatically generating behavioral models of analog circuits with parameter variations. My thesis is on the adaptation of Support Vector Machines to generate models with interval-valued parameters. I received my doctorate degree from the University of Hannover in 2019. In 2016, I joined Robert Bosch GmbH Corporate Research as a research engineer. I joined the Chair X (Data Science) in 2019 as a post-doctoral researcher.

    Projects and Research Interests

    I am currently doing research in Environmental Sensing and Time Series Analysis. I am interested in furthering methods to enhance existing physics-based models - such as meteorological models, and to further our understanding based on data obtained by sparse and dynamic sensor networks.


    Introductions to algorithms and data structures:

    Data Mining:


    • 1.
      Davidson, P., Steininger, M., Lautenschlager, F., Kobs, K., Krause, A., Hotho, A.: Anomaly Detection in Beehives using Deep Recurrent Autoencoders. Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020). pp. 142–149. SCITEPRESS – Science and Technology Publications, Lda (2020).
    • 2.
      Dulny, A., Steininger, M., Lautenschlager, F., Krause, A., Hotho, A.: Evaluating the multi-task learning approach for land use regression modelling of air pollution. International Conference on Frontiers of Artificial Intelligence and Machine Learning. IASED (2020).
    • 3.
      Schlör, D., Ring, M., Krause, A., Hotho, A.: Financial Fraud Detection with Improved Neural Arithmetic Logic Units. (2020).
    • 4.
      Lautenschlager, F., Becker, M., Kobs, K., Steininger, M., Davidson, P., Krause, A., Hotho, A.: OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning. Atmospheric Environment. 233, 117535 (2020).