Personen

Prof. Dr. Stefanie Vogl

Zum Personenverzeichnis

Machine Learning, Applied Statistics

Lehre

Forschung

  • Digitalization of Handwritten Documents with Machine Learning in Cooperation with "Schweizerische Vogelwarte Sempach"


Werdegang

  • Prof. für Machine Learning


Veröffentlichungen

  • Beck, N., Dovern, J. & Vogl, S. (2025). Mind the Naive Forecast! A Rigorous Evaluation of Forecasting Models for Time Series with Low Predictability, Applied Intelligence (APIN), in press

    Polz, J., Glawion, L., Gebisso, H., Altenstrasser, L., Graf, M., Kunstmann, H., Vogl, S. & Chwala, C. (2024). Temporal Super-Resolution, Ground Adjustment and Advection Correction of Radar Rainfall using 3D-Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing.

    Pagel, J., Vogl, S., & Israel, L. S. (2024). Analyzing the impact of redaction on document classification performance of deep CNN models. International Journal on Document Analysis and Recognition (IJDAR), 1-13.

    Schramm, S., Pieper, M., & Vogl, S. (2023). Orthogonal Procrustes and Machine Learning: Predicting Bill of Materials errors on time. Computers & Industrial Engineering, 185, 109606.

    Cotorogea, B. P. F., Marino, G., & Vogl, S. (2022). Data driven health monitoring of Peltier modules using machine-learning-methods. SLAS technology.

    Vogl, S., Laux, P., Bialas, J., & Reifenberger, C. (2022). Modelling precipitation intensities from x-band radar measurements using Artificial Neural Networks—a feasibility study for the Bavarian Oberland region. Water, 14(3), 276.

    Schwab, E., Pogrebnoj, S., Freund, M., Flossmann, F., Vogl, S., & Frommolt, K. H. (2022). Automated bat call classification using deep convolutional neural networks. Bioacoustics, 1-16.

    Plonus, R. M., Vogl, S., & Floeter, J. (2021). Automatic Segregation of Pelagic Habitats. Frontiers in Marine Science, 1447.

    Hussak, J., Vogl, S., Grothmann, R., & Weber, M. (2018): Prognosis of EPEX SPOT Electricity Prices Using Artificial Neural Networks. In Operations Research Proceedings 2017 (pp. 89-94). Springer, Cham.

    Mao, G., Vogl, S., Laux, P., Wagner, S., and H. Kunstmann, (2015): Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data. Hydrol. Earth Syst. Sci., 19, 1787-1806. doi:10.5194/hess-19-1787-2015.

    Vogl, S., Laux, P., Qiu, W., Mao, G., and H. Kunstmann, (2012): Copula-based assimilation of radar and gauge information to derive bias corrected precipitation fields. Hydrol. Earth Syst. Sci., 16, 2311-2328. doi:10.5194/hess-16-2311-2012.

    Kraller, G., Warscher, M., Kunstmann, H., Vogl, S., Marke, T., and U. Strasser, (2012). Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany). Hydrol. Earth Syst. Sci., 16, 1969-1990. doi:10.5194/hess- 16-1969-2012.

    Sanchez-Lorenzo, A., Laux, P., Hendricks-Franssen, H.-J., Calbó, J., Vogl, S., Georgoulias, A. K., and J. Quaas, (2012). Assessing large-scale weekly cycles in meteorological variables: a review.
    Atmos. Chem. Phys., 12, 5755-5771. doi:10.5194/acp-12-5755-2012.

    Laux, P., Vogl, S., Qiu, W., Knoche, H. R., and H. Kunstmann, (2011). Copula-based statistical refinement of precipitation in RCM simulations over complex terrain.
    Hydrol. Earth Syst. Sci., 15, 1-19.

    Vogl, S., (2009): "Tropical Cyclone Boundary-Layer Models". Dissertation, Ludwig Maximilians Universität, München

    Vogl, S., and R. K. Smith, (2009). Limitations of a linear model for the hurricane boundary layer. Q. J. R. Meteorol. Soc., 135, 839-850.

    Smith, R. K., Montgomery, M. T., & Vogl, S. (2008). A critique of Emanuel‘s hurricane model and potential intensity theory. Q. J. R. Meteorol. Soc., 134, 551-561.

    Smith, R. K., & Vogl, S. (2008). A simple model of the hurricane boundary layer revisited. Q. J. R. Meteorol. Soc., 134, 337-351.


  • A. Gigler, S. Vogl et al., (2016): „Räumliche Selektion und Rückprojektion bei Hyperspectral Imaging“, PCT/EP2016/072953.

    S. Vogl, A. Gigler, et al, (2016): „Fortgeschrittene prädiktive Analyse durch Ensemble Entscheidungen“, PCT/EP2016/072977.

    A. Gigler, S. Vogl, et al., (2016): „Robuste und zuverlässige prädiktive Analyse durch Wahl essentieller Spektralbereiche“, 10 2016 218 520.9

    S. Vogl. R. Grothmann, H.G. Zimmermann, (2016): „Deep Error Correction Neural Networks for an improved modeling of sensor data with systematic residual error behaviour“, Schutzrecht angemeldet.

    S. Vogl, K. Heesche, H.G. Zimmermann, (2016): „RNN Architekturen für spärlich besetzte Zeitreihen“, 10 2016 216 402.3.

    S. Vogl, R. Grothmann, H.G. Zimmermann, (2015): „Smart Grid Optimization using Smart Meter Forecasts by Neural Predictive Clustering“, PCT/EP2017/062239 sowie 10 2016 209 721.0.

    S. Vogl, et al., (2014): „Data Analytics for Lab-on-a-Chip“, PCT/EP2015/065097.


  • Sandro Scheid und Stefanie Vogl (2021): „Data Science – Grundlagen, Methoden und Modelle der Statistik“, Carl Hanser Verlag GmbH Co KG

    Sanchez-Lorenzo, A., Laux, P., Hendricks-Franssen, H.-J., Georgoulias, A. K., Calbó, J., Vogl, S., and J. Quaas, (2012):
    "Weekly cycles in meteorological variables over large-scales: fact or myth?". Advances in Meteorology, Climatology and Atmospheric Physics, Proceedings of the 11th International Conference on Meteorology, Climatology and Atmospheric Physics, Athens, Greece, 29 May-1 June, 2012, Helmis C.G. and Nastos P. (Eds.),
    Springer Atmospheric Sciences, Springer-Verlag, Berlin, Heidelberg, 1211- 1217.