piwik-script

Deutsch Intern
    Data Science Chair

    Dr.-Ing. Anna Krause

    Chair of Data Science (Informatik X)
    University of Würzburg
    Am Hubland
    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 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, 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 ]
    • 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 (2023). 37(1) 136–166.
    • Feature relevance XAI in anomaly detection: Reviewing approaches and challenges Tritscher, Julian; Krause, Anna; Hotho, Andreas in Frontiers in Artificial Intelligence (2023). 6
    2022[ to top ]
    • 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.) (2022). 1–20.
    • 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.) (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, Lecture Notes in Computer Science, R. Bergmann, L. Malburg, S. C. Rodermund, I. J. Timm (eds.) (2022). (Vol. 13404) 75–89.
    • Open ERP System Data For Occupational Fraud Detection Tritscher, Julian; Gwinner, Fabian; Schlör, Daniel; Krause, Anna; Hotho, Andreas (2022).
    • Semi-unsupervised Learning for Time Series Classification Davidson, Padraig; Steininger, Michael; Huhn, André; Krause, Anna; Hotho, Andreas in Milets@KDD (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 (2022). Communications in Computer and Information Science(1753) 79–96.
    2021[ to top ]
    • A financial game with opportunities for fraud Tritscher, Julian; Krause, Anna; Schl{\"o}r, Daniel; Gwinner, Fabian; von Mammen, Sebastian; Hotho, Andreas in IEE COG 2021 (2021). 2021
    • Anomaly Detection in Beehives: An Algorithm Comparison Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas (2021).
    • Density-based weighting for imbalanced regression Steininger, Michael; Kobs, Konstantin; Davidson, Padraig; Krause, Anna; Hotho, Andreas in Machine Learning, (A. Appice; S. Escalera; J. A. Gamez; H. Trautmann, eds.) (2021). 110(8) 2187–2211.
    • DETECTING PRESENCE OF SPEECH IN ACOUSTIC DATA OBTAINED FROM BEEHIVES Janetzky, Pascal; Davidson, Padraig; Steininger, Michael; Krause, Anna; Hotho, Andreas in DCASE Workshop (2021).
    • 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 (2021). 1834(1) 012004.
    • NeuralPDE: Modelling Dynamical Systems from Data Dulny, Andrzej; Hotho, Andreas; Krause, Anna (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 (2021). 34–44.
    • 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{\"o}ller, E. Rueckert (eds.) (2021). 51–66.
    2020[ to top ]
    • 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) (2020). 142–149.
    • 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).
    • 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 (2020).
    • Financial Fraud Detection with Improved Neural Arithmetic Logic Units Schlör, Daniel; Ring, Markus; Krause, Anna; Hotho, Andreas (2020). (Vol. Fifth Workshop on MIning DAta for financial applicationS)
    • 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 (2020). 233 117535.