piwik-script

Intern
    Data Science Chair

    Michael Steininger, M.Sc.

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

    Email:  steininger@informatik.uni-wuerzburg.de

    Phone: (+49 931)  31 - 89482

    Office: Room B108 (Computer Science Building M2)

    Projects and Research Interests

    I joined the DMIR group for my PhD studies after receiving my master's degree in Computer Science at the university of Würzburg in early 2018. As a member of the BigData@Geo project, I am working on climate and air pollution modeling using Deep Learning techniques. I am also interested in methods for tackling data imbalance in regression tasks.

    Teaching

    Summer Term 2021:

    Summer Term 2020:

    Summer Term 2019:

    Winter Term 2018/19:

    Summer Term 2018:

    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 (link)

    Publications

    2021

    • Anomaly Detection in Beeh... - Download
      Anomaly Detection in Beehives: An Algorithm Comparison P. Davidson; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho (2021).
       
    • Semi-unsupervised Learning: An In-depth Parameter Analysis P. Davidson; F. Buckermann; M. Steininger; A. Krause; A. Hotho S. Edelkamp, R. Möller, E. Rueckert (Eds.) (2021). 51–66.
       
    • Semi-Supervised Learning for Grain Size Distribution Interpolation K. Kobs; C. Schäfer; M. Steininger; A. Krause; R. Baumhauer; H. Paeth; A. Hotho (2021). 34–44.
       
    • Density-based weighting f... - Download
      Density-based weighting for imbalanced regression M. Steininger; K. Kobs; P. Davidson; A. Krause; A. Hotho in Machine Learning, (A. Appice; S. Escalera; J. A. Gamez; H. Trautmann, Eds.) (2021). 110(8) 2187–2211.
       
    • Evaluating the multi-task... - Download
      Evaluating the multi-task learning approach for land use regression modelling of air pollution A. Dulny; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho in Journal of Physics: Conference Series (2021). 1834(1) 012004.
       

    2020

    • Deep Learning for Climate... - Download
      Deep Learning for Climate Model Output Statistics M. Steininger; D. Abel; K. Ziegler; A. Krause; H. Paeth; A. Hotho in NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning (2020).
       
    • Anomaly Detection in Beeh... - Download
      Anomaly Detection in Beehives using Deep Recurrent Autoencoders P. Davidson; M. Steininger; F. Lautenschlager; K. Kobs; A. Krause; A. Hotho (2020). 142–149.
       
    • OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning F. Lautenschlager; M. Becker; K. Kobs; M. Steininger; P. Davidson; A. Krause; A. Hotho in Atmospheric Environment (2020). 233 117535.
       
    • MapLUR: Exploring a New P... - Download
      MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images M. Steininger; K. Kobs; A. Zehe; F. Lautenschlager; M. Becker; A. Hotho in ACM Trans. Spatial Algorithms Syst. (2020). 6(3)
       
    • SimLoss: Class Similarities in Cross Entropy K. Kobs; M. Steininger; A. Zehe; F. Lautenschlager; A. Hotho (2020). 431–439.
       

    2018

    • EveryAware Gears: A Tool ... - Download
      EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data F. Lautenschlager; M. Becker; M. Steininger; A. Hotho D. Burghardt, S. Chen, G. Andrienko, N. Andrienko, R. Purves, A. Diehl (Eds.) (2018).
       

    Other scientific activities

    • Reviewer for Nature Energy
    • PC Member for Tackling Climate Change with Machine Learning Workshop at ICML 2021 and NeurIPS 2021
    • Subreviewer for several conferences and journals