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.
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.
- PC member for ECMLPKDD 2021 (Research TracK)
- PC member for the MIDAS workshop 2021
- Reviewer for Volkswagen Stiftung
- 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
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.Steininger, M., Abel, D., Ziegler, K., Krause, A., Paeth, H., Hotho, A.: Deep Learning for Climate Model Output Statistics. Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020. (2020).
3.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).
4.Schlör, D., Ring, M., Krause, A., Hotho, A.: Financial Fraud Detection with Improved Neural Arithmetic Logic Units. (2020).
5.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).