Hyunglok Kim bio photo

Hyunglok Kim

Terrestrial Water Cycle
Remote Sensing
Bayesian Inference
Machine Learning
Deep Learning

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I am a PhD candidate at the Department of Engineering Systems and Environment at the University of Virginia where I am studying hydrology, applications of Bayes’ theorem, and global-scale water cycles. The overarching goal of my research is to provide accurate hydrological variables and solve the major challenges related to Earth system science that we will face in the coming decades. My work has practical applications for improving the quantity and quality of satellite- and model-based data to more accurately predict natural disasters and to provide a better understanding of the role played by hydrometeorological factors. You can find several of the many intriguing projects on which I am working by clicking this link .
You can also veiw my Google Scholar record to learn more about my work. You can contact me via hk5kp at virginia dot edu.

Current Interests:

  • Applications of Deep Learning to Hydrology
  • Bayesian Inference
  • Remote Sensing (Radiometer, GNSS, and Radar)
  • Global Water Cycle
  • Natural Disasters: Urban Flooding, Droughts, and Dust Outbreaks

Current Projects:

  • Soil moisture retrievals from satellites (passive, active, and GPS signals), reanalysis data, and in situ sensors.
  • Satellite-based soil moisture data assimilation into land surface models.
  • Impact of human activities (irrigation, crop modifications, etc.) on global soil moisture dynamics.
  • GitHub Pages