Max Planck Gesellschaft

Basil Kraft

Doctoral researcher with the Global Diagnostic Modelling Research Group

room: C3.017 (tower)
phone: +49 3641 57 6233
email: bkraft(at)bgc-jena.mpg.de







Research interests

  • Environmental modelling
  • Remote sensing
  • Time series
  • Deep learning
  • Recurrent Neural Networks

PhD project

Still today, many machine-learning approaches fail in encoding temporal memory effects that typically characterize dynamical natural systems. Hence, ecosystem and Earth System dynamics are often hard to model and predict. Yet, today modern deep learning approaches in the time domain (e.g., Recurrent Neural Networks) are theoretically prepared to address such issues. Specifically, the aim of this PhD project is therefore to pioneer and to scrutinize the potential of such methods and their combinations to accurately describe, understand, and predict Earth System variables considering memory effects.


Conferences & workshops

  • 2020 - EGU talk "Towards global hybrid hydrological modeling by fusing deep learning and a conceptual model"
  • 2019 - Workshop on ecological memory effects (talk, participant), Oak Ridge Natiaonal Labs (ORNL)
  • 2019 - Introduction to Deep Learning (talk), Institute of Coastal Research, HZG
  • 2019 - EGU talk "Identifying dynamic memory effects on vegetation state using recurrent neural networks"
  • 2019 - Workshop "Deep Learning 101" (host) at the Earth System PhD Conference (ESPC) at the Max Planck Institute for Biogeochemistry
  • 2019 - Co-organizer of the Earth System PhD Conference (ESPC) at the Max Planck Institute for Biogeochemistry
  • 2018 - Co-chair of the "Deep Learning for Environmental Science & Ecology" session at the International Conference on Ecological Informatics (ICEI)

Publications

Kraft, B., Jung, M., Körner, M., and Reichstein, (2020, accepted). M. Hybrid modeling: fusion of a deep learning approach and a physics-based model for global hydrological modeling. ISPRS archives.

Kraft, B., Jung, M., Körner, M., Requena Mesa, C., Cortés, J., and Reichstein, M., (2019). Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks. Frontiers in Big Data, 2, p.31.

Reichstein, M., Besnard, S., Carvalhais, N., Gans, F., Jung, M., Kraft, B., and Mahecha, M. (2018). Modelling Landsurface Time-Series with Recurrent Neural Nets. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 7640-7643). IEEE.

Requena-Mesa, C., Reichstein, M., Mahecha, M., Kraft, B., and Denzler, J. (2018). Predicting landscapes as seen from space from environmental conditions. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 1768-1771). IEEE.


Academic background

2017-present

Member of the Global Diagnostic Modelling Research Group at the Max Planck Institute for Biogeochemistry in Jena and external PhD student at the Chair of Remote Sensing Technology (Computer Vision Research Group), Technical University of Munich

2015-2017

MSc in Geography (Specialization in Remote Sensing, GIS and Applied Statistics), University of Zurich (CH)

2010-2014

BSc in Geography, University of Zurich (CH)


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