Intern
    Data Science Chair

    Dr.-Ing. Anna Krause

    Chair of Data Science (Informatik X)
    University of Würzburg
    Campus Hubland Nord
    Emil-Fischer-Straße 50
    97074 Würzburg
    Germany

    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 50.03.008 (Institutsgebäude Künstliche Intelligenz)

    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, where I am currently leading the Deep Learning for Dynamical Systems Group.

    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.

    Teaching

    Activities

    Awards

    • Best ML Innovation Award: "Deep Learning for Climate Model Output Statistics", Michael Steininger, Daniel Abel, Katrin Ziegler, Anna Krause, Heiko Paeth, Andreas Hotho at Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020 (link)
    • Best Student Paper Award: "Evaluating the multi-task learning approach for land use regression modelling of air pollution", Andrzej Dulny, Michael Steininger, Florian Lautenschlager, Anna Krause, Andreas Hotho at FAIML 2020
    • Best Paper Award: "Financial Fraud Detection with Improved Neural Arithmetic Logic Units" by Daniel Schlör, Markus Ring, Anna Krause, Andreas Hotho on the Fifth Workshop on MIning DAta for financial applicationS Co-Hosted by ECML- PKDD 2020

    Publications

    2023[ to top ]
    • Feature relevance XAI in anomaly detection: Reviewing approaches and challenges. Tritscher, Julian; Krause, Anna; Hotho, Andreas. In Frontiers in Artificial Intelligence, 6. 2023.
    • DynaBench: A benchmark da...
      DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. 2023.
    • TaylorPDENet: Learning PD...
      TaylorPDENet: Learning PDEs from non-grid Data. Heinisch, Paul; Dulny, Andrzej; Krause, Anna; Hotho, Andreas. In Workshop on Neuro-Explicit AI and Expert-Informed Machine Learning for Engineering and Physical Sciences at the ECML PKDD 2023. 2023.
    • Swarming Detection in Sma...
      Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data. Janetzky, Pascal; Schaller, Melanie; Krause, Anna; Hotho, Andreas. In International Conference on Systems, Signals and Image Processing (IWSSIP). 2023.
    • Automatic Speech Detectio...
      Automatic Speech Detection on a Smart Beehive’s Raspberry Pi. Janetzky, Pascal; Lissmann, Philipp; Hotho, Andreas; Krause, Anna. In LWDA 2023 - Learning, Knowledge, Data, and Analysis (accepted). 2023.
    • ConvMOS: climate model output statistics with deep learning. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In Data Mining and Knowledge Discovery, 37(1), pp. 136–166. 2023.
    • Occupational Fraud Detect...
      Occupational Fraud Detection through Agent-based Data Generation. Tritscher, Julian; Roos, Alexander; Schlör, Daniel; Hotho, Andreas; Krause, Anna. In MIDAS - The 8th Workshop on MIning DAta for financial applicationS at the European Conference on Machine Learning and Principles and Practives of Knowledge Discovery in Databases (ECMLPKDD), accepted. 2023.
    2022[ to top ]
    • Semi-unsupervised Learning for Time Series Classification. Davidson, Padraig; Steininger, Michael; Huhn, André; Krause, Anna; Hotho, Andreas. In Milets@KDD. arXiv, 2022.
    • Open ERP System Data For Occupational Fraud Detection. Tritscher, Julian; Gwinner, Fabian; Schlör, Daniel; Krause, Anna; Hotho, Andreas. 2022.
    • NeuralPDE: Modelling Dynamical Systems from Data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. In {KI} 2022: Advances in Artificial Intelligence - 45th German Conference on AI, Trier, Germany, September 19-23, 2022, Proceedings, Vol. 13404 of Lecture Notes in Computer Science, R. Bergmann, L. Malburg, S. C. Rodermund, I. J. Timm (eds.), pp. 75–89. Springer, 2022.
    • Towards Explainable Occupational Fraud Detection. Tritscher, Julian; Schlör, Daniel; Gwinner, Fabian; Krause, Anna; Hotho, Andreas. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Communications in Computer and Information Science(1753), pp. 79–96. 2022.
    • Anomaly Detection in Bee...
      Anomaly Detection in Beehives: An Algorithm Comparison. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In Sensor Networks, A. Ahrens, R. V. Prasad, C. Benavente-Peces, N. Ansari (eds.), pp. 1–20. Springer International Publishing, Cham, 2022.
    • ConvMOS: Climate Model Output Statistics with Deep Learning. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In Data Mining and Knowledge Discovery, P. Cellier; K. Dembczynski; A. Zimmermann; E. Devijver (eds.). Springer, 2022.
    2021[ to top ]
    • Evaluating the multi-task learning approach for land use regression modelling of air pollution. Dulny, Andrzej; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In Journal of Physics: Conference Series, 1834(1), p. 012004. {IOP} Publishing, 2021.
    • NeuralPDE: Modelling Dynamical Systems from Data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. 2021.
    • DETECTING PRESENCE OF SPEECH IN ACOUSTIC DATA OBTAINED FROM BEEHIVES. Janetzky, Pascal; Davidson, Padraig; Steininger, Michael; Krause, Anna; Hotho, Andreas. In DCASE Workshop. 2021.
    • Density-based weighting for imbalanced regression. Steininger, Michael; Kobs, Konstantin; Davidson, Padraig; Krause, Anna; Hotho, Andreas. In Machine Learning, 110(8), A. Appice; S. Escalera; J. A. Gamez; H. Trautmann (eds.), pp. 2187–2211. 2021.
    • Semi-Supervised Learning for Grain Size Distribution Interpolation. Kobs, Konstantin; Schäfer, Christian; Steininger, Michael; Krause, Anna; Baumhauer, Roland; Paeth, Heiko; Hotho, Andreas. In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI, pp. 34–44. Springer International Publishing, 2021.
    • Semi-unsupervised Learning: An In-depth Parameter Analysis. Davidson, Padraig; Buckermann, Florian; Steininger, Michael; Krause, Anna; Hotho, Andreas. In KI 2021: Advances in Artificial Intelligence, S. Edelkamp, R. M{ö}ller, E. Rueckert (eds.), pp. 51–66. Springer International Publishing, Cham, 2021.
    • Anomaly Detection in Beehives: An Algorithm Comparison. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. 2021.
    • A financial game with opportunities for fraud. Tritscher, Julian; Krause, Anna; Schl{ö}r, Daniel; Gwinner, Fabian; von Mammen, Sebastian; Hotho, Andreas. In IEE COG 2021, 2021. 2021.
    2020[ to top ]
    • Evaluating the multi-task learning approach for land use regression modelling of air pollution. Dulny, Andrzej; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In International Conference on Frontiers of Artificial Intelligence and Machine Learning. IASED, 2020.
    • Anomaly Detection in Beehives using Deep Recurrent Autoencoders. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Kobs, Konstantin; Krause, Anna; Hotho, Andreas. In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pp. 142–149. SCITEPRESS – Science and Technology Publications, Lda., 2020.
    • Financial Fraud Detection with Improved Neural Arithmetic Logic Units. Schlör, Daniel; Ring, Markus; Krause, Anna; Hotho, Andreas. Vol. Fifth Workshop on MIning DAta for financial applicationS. 2020.
    • Deep Learning for Climate Model Output Statistics. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning. 2020.
    • OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning. Lautenschlager, Florian; Becker, Martin; Kobs, Konstantin; Steininger, Michael; Davidson, Padraig; Krause, Anna; Hotho, Andreas. In Atmospheric Environment, 233, p. 117535. 2020.