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Mitarbeiterbild von Felix Riese

M.Sc. Felix Riese

Wissenschaftlicher Mitarbeiter
room: 002 (Geb. 20.52)
phone: +49 721 608 47304
fax: +49 721 608 48450
felix rieseLfh0∂kit edu



Curriculum vitae

Teaching

Research interests

Other profiles

Talks and posters

Publications

Projects

 

Curriculum vitae

July 2018 VISUM Summer School in Porto (PT) about machine learning and computer vision
Since 01/2018 MBA fellow at the Collège des ingénieurs (CDI) in Paris, France
Since 05/2017 PhD student at the Institute for Photogrammetry and Remote Sensing of the Karlsruhe Institute of Technology (KIT)
Since 2016 Lecturer for mathematics at the Baden-Wuerttemberg Cooperative State University (DHBW) in the degree programm "computer science"
10/2014 - 03/2017 Master of Science in physics at the KIT, major in "data analysis in particle physics"
  Masterthesis: "Boosted-Jet Reconstruction Methods in a Search for Higgs-Boson Production in Association with a Top-Quark-Antiquark Pair at the CMS Experiment" at the Institute of Experimental Nuclear Physics (EKP) of the KIT with Prof. Dr. Husemann
10/2011 - 09/2014 Bachelor of Science in physics at the KIT

 

Teaching

  • Introduction to linear algebra, analysis and statistics at the DHBW Karlsruhe
  • Introduction to knowledge-based systems at the DHBW Karlsruhe
  • Introduction to LaTeX at the DHBW Karlsruhe

 

Research interests

  • Machine Learning: Data Analysis, Supervised and Unsupervised Learning, Self-Organizing Maps (SOM), Deep Learning, Python
  • Hyperspectral Remote Sensing, Multispectral Satellites (Sentinel-2), UAV
  • Geoscience: estimation of soil surface parameters

 

Other profiles

  • Professional network via LinkedIn
  • Code & datasets at GitHub
  • Publications at GoogleScholar
  • Publications at ResearchGate

 

Talks and posters

July 2018 Poster about "Self-organizing maps for regression with hyperspectral data" at VISUM summer school
March 2018 Talk about the publication "Modeling Subsurface Soil Moisture Based on Hyperspectral Data - First Results of a Multilateral Field Campaign" (see below)

Publikationen


2018
Keller, S.; Riese, F. M.; Allroggen, N.; Jackisch, C.; Hinz, S. (2018). Modeling Subsurface Soil Moisture Based on Hyperspectral Data : First Results of a Multilateral Field Campaign. Photogrammetrie, Fernerkundung, Geoinformatik, Kartographie 2018 (PFGK18) : Beiträge der 37. Wissenschaftlich-Technische Jahrestagung der DGPF e.V., 5. Münchner GI-Runde Runder Tisch GIS e.V. und des 66. Deutscher Kartographie Kongress der DGfK e.V., München, Deutschland, 7. - 9. März 2018. Hrsg.: T. P. Kersten, 34-48, Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation, München.
Keller, S.; Riese, F. M.; Stötzer, J.; Maier, P. M.; Hinz, S. (2018). Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data.
Projects
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