Sciences and Exploration Directorate

Jordan Alexis Caraballo-Vega



Org Code: 587

Mail Code: 606.2
Greenbelt, MD 20771

Employer: NASA

Brief Bio

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, 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-aided data structures to support large-scale satellite data handling, deep learning applications to streamline analysis processes, and the portability of software to support open science initiatives via cloud and on-premises environments. Recent work includes very high-resolution land cover land use change (LCLUC) mapping, wildfire modeling, and vegetation structure analysis by employing deep learning techniques. 


Computer Engineer, AST

Science Data Processing Branch, CISTO Data Science Group, Goddard Space Flight Center - Greenbelt, Maryland

January 2020 - Present

Development of AI, high-performance computing, and GPU-accelerated software for the analysis of remotely sensed data.

Pathways Engineering Trainee

Science Data Processing Branch, Goddard Space Flight Center - Greenbelt, MD

August 2018 - December 2019

Development of software to support science data processing research.

Computational Research Assistant

Partnership for Research and Education in Materials (PREM), NSF - Humacao, Puerto Rico

2013 - 2020

Led a team of five undergraduate students in the development of material science computational projects. Developed and implemented software to accelerate molecular dynamics simulations in HPC environments. Assisted and collaborated with ongoing research projects in the area of data mining, clustering, image processing, and machine learning. Studied biological and electrical systems applied to physical sensors by means of molecular dynamics simulations.

Computer Science Trainee

NASA Center for Climate Simulation, Goddard Space Flight Center - Greenbelt, Maryland

2016 - 2018

Intern: Summer 2016, Fall 2016, Summer 2017, Summer 2018; Contractor under ADNET LLC: Spring 2017, Fall 2017, Spring 2018;

Responsible for the engineering of software and information systems to support a critical log analysis infrastructure for monitoring and anomaly detection of high-performance computing systems. Served as a Linux and Unix system administrator for security and resource intensive systems, including revision control, networking, and user-facing virtual machines. Designed and developed software to support several computing intensive applications via multi-node MPI implementations and DevSecOps initiatives.


2022 – Present  PhD Geographical Sciences, University of Maryland, College Park, USA.

2015 – 2020 B.S. Computational Mathematics, Major Computer Science, University of Puerto Rico at Humacao, PR.

Professional Societies

American Geophysical Union (AGU)


2021 - Present

NASA Intelligent Systems for Data Analysis Technologies (ISDAT)


2018 - Present

NASA Hispanic Advisory Committee for Employees (HACE)


2016 - Present

UPRH Consortium of Mathematics and Computer Science (ASMACC)


2015 - 2020

Materials Research Society (MRS)


2014 - Present


  • 2021 - Employee Contribution Award, SEWP, NASA HQ
  • 2021 - Special Act Award, CISTO, NASA GSFC
  • 2020 - Group Achievement Award, ISDAT, NASA GSFC
  • 2019 - Robert H. Goddard Exceptional Achievement for Mission Support, NCCS, NASA GSFC
  • 2017 - John Mather Scholarship Awardee
  • 2015 - NASA Minority University Research Education Program Scholarship Awardee
  • 2015 - Brystol Myers Squib Industry Excellence in Science and Math Scholarship Awardee
  • 2013 - Partnership for Research and Education in Materials Research Fellowship Awardee



Le, M. T., K. Wessels, J. Caraballo-Vega, et al. N. Thomas, M. Wooten, M. Carroll, and C. Neigh. 2023. Training Strategies of CNN for Land Cover Mapping with High Resolution Multi-Spectral Imagery in Senegal IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium [10.1109/igarss52108.2023.10283308]

Spradlin, C., M. Wooten, J. A. Caraballo-Vega, et al. M. L. Carroll, C. S. Neigh, K. Wessels, M. T. Le, P. Montesano, W. Alemu, and N. Thomas. 2023. Large-Scale Distributed Compositing and Statistics Framework For Very-High-Resolution Remote Sensing Imagery IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium [10.1109/igarss52108.2023.10283222]

Montesano, P., M. Carroll, C. Neigh, et al. M. Macander, J. Caraballo-Vega, G. Frost, and G. Tamkin. 2023. Producing a Science-Ready Commercial Data Archive: A Workflow for Estimating Surface Reflectance for High Resolution Multispectral Imagery IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium [10.1109/igarss52108.2023.10281820]

Alemu, W. G., C. S. Neigh, J. A. Caraballo-Vega, et al. M. R. Wooten, E. Muluken, G.-M. Maru, and C. Mulu. 2023. Land Cover Mapping in the Amhara Region of Northwest Ethiopia Using Convolutional Neural Networks and Domain Adaptation Techniques IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium [10.1109/igarss52108.2023.10283273]

Mullen, A. L., J. D. Watts, B. M. Rogers, et al. M. L. Carroll, C. D. Elder, J. Noomah, Z. Williams, J. A. Caraballo‐Vega, A. Bredder, E. Rickenbaugh, E. Levenson, S. W. Cooley, J. K. Hung, G. Fiske, S. Potter, Y. Yang, C. E. Miller, S. M. Natali, T. A. Douglas, and E. D. Kyzivat. 2023. Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies Geophysical Research Letters 50 (7): [10.1029/2022gl102327]

Caraballo-Vega, J., M. Carroll, C. Neigh, et al. M. Wooten, B. Lee, A. Weis, M. Aronne, W. Alemu, and Z. Williams. 2023. Optimizing WorldView-2, -3 cloud masking using machine learning approaches Remote Sensing of Environment 284 113332 [10.1016/j.rse.2022.113332]

Caraballo-Vega, J. A., N. S. Smith, M. L. Carroll, et al. L. Carriere, J. E. Jasen, M. T. Le, J. Li, K. Peck, S. L. Strong, G. S. Tamkin, M. A. Thompson, and J. H. Thompson. 2022. Remote Sensing Powered Containers for Big Data and AI/ML Analysis: Accelerating Science, Standardizing Operations IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium [10.1109/igarss46834.2022.9883436]