Jordan Alexis Caraballo-Vega is a Computer Engineer with the Science Data Processing branch and the Computational and Information Sciences and Technology Office (CISTO) Data Science Group at the NASA Goddard Space Flight Center. His research focuses on the development of software to support science research in the areas of Earth observation, artificial intelligence, computational material science, and high-performance computing. Caraballo’s main interest is to shorten the gap between Earth science and artificial intelligence by means of hardware accelerated software applications, which include the development of GPU-backed data structures to support satellite data formats, deep learning applications to streamline annotation processes, and the portability of software to support open science initiatives via cloud and on-premises environments. Recent work includes the development of large-scale machine learning and deep learning applications for land cover land use change (LCLUC), object detection, and semantic segmentation of very high-resolution multi-spectral satellite imagery.

Earth Observation applied to agricultural modeling, focus on data sparse regions