1Jet Propulsion Laboratory, California Institute of Technology
JPL has a long history of building many innovative solutions for onboard instrument, ground operation and data system, archive and distribution for our missions. As the rate of data generate from our missions continue to increase and is expected to rise significantly in near future, JPL is engaging data science and artificial intelligence technologies and methodologies for mission operations and to enable science. In recent years, JPL made significant advancement to improve Earth science through machine learning, intelligent search, data fusion, interactive visualization and analytics. This talk presents some of the data science highlights as JPL’s ongoing effort in delivering operation-quality analytics solutions to mission operation and our science communities.
Thomas Huang is a Technical Group Supervisor for the JPL’s Computer Science for Data-Intensive Applications group. He is also the Strategic Lead for Interactive Analytics for the National Space Technology Applications Program Office, the Principal Investigator on several NASA Cloud-based big data analytic projects, and the System Architect for the NASA’s Sea Level Change Portal. As an expert in large-scale, distributed intelligent data systems, Thomas led planetary, earth data system, and defense research projects. Thomas was the Project Technologist for the NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC). As an advocate for free and open source software, Thomas led the open sourcing of many NASA-funded technologies. He recently established the Apache Science Data Analytics Platform (SDAP) as a community-driven, Cloud-based Analytic Center Framework. Thomas is a Computer Science lecturer at the California State Polytechnic University, Pomona, and a member of its Industry Advisory Board.