J. Emmanuel Johnson is a 4th year PhD student at the Universitat of Valencia under the tutelage of Dr. Valero Laparra and Dr. Gustau Camps-Valls. During my time here, he’s worked with derivatives of kernel methods; in particular regression models. Applications of this include propagation input error in Gaussian process (GP) regression models and input sensitivity analysis of radiative transfer model emulators using GPs. He’s also been working closely with RBIG (an information theoretic-based approach for density estimation) and worked on some experiments regarding IT measures. Some applications from this include spatial-temporal analysis of Earth science data, related to Normalizing flows, and drought detection. His future goals are to continue to develop and apply new methods with methods involving uncertainty, interpretability and exploratory data analysis. And he would like to really focus on the applied section on problems related to climate, the ocean, and possibly the medical. I also really like coding. So I’m always looking at new packages and trying a few out myself.


  • Uncertainty Quantification
  • Gaussian Processes
  • Normalizing Flows
  • Information Theory
  • Oceanography
  • Climate


  • PhD in Electrical Engineering (Candidate), 2021

    Universitat de Valencia

  • MS in Computational and Applied Mathematics, 2016

    Rochester Institute of Technology

  • BS in Mathematical Sciences, 2013

    Florida Institute of Technology

  • BS in Physical Oceanography, 2013

    Florida Institute of Technology