Virtual Workshop 2026 - AI Meets CI - Day 3 - Talk IV: AI Foundation Models for Science, Rahul Ramachandran, from NASA
Talk IV: AI Foundation Models for Science, Rahul Ramachandran, Senior Research Scientist, NASA's Marshall Space Flight Center
Abstract: AI Foundation Models for Science
NASA manages petabytes of observational data, spanning scales from galactic structures to microscopic biological systems. While these archives offer unparalleled opportunities for scientific discovery, their sheer volume challenges traditional extraction of meaningful insights. Conventional deep learning addresses these issues but remains constrained by the resource-intensive requirement for large, labeled datasets. Foundation Models (FMs) offer an alternative by employing self-supervised learning to distinguish latent patterns within unlabeled data, thereby streamlining diverse downstream applications.
To operationalize this potential, NASA’s Office of the Chief Science Data Officer implemented a strategy to develop open-source FMs using flagship datasets from all science divisions, alongside a science-specific language model. Key achievements include two iterations of the Prithvi Geospatial model for environmental monitoring and the Prithvi Weather and Climate model, designed to reconstruct atmospheric states and forecast future conditions. Additionally, the initiative recently deployed a heliophysics foundation model to advance space weather analysis. This presentation will provide an overview of the foundation models released to date, the workflows used in their design and development, and the roadmap for future models.
The NSF CI Compass virtual workshop - AI Meets CI: Intelligent Infrastructure for Major & Midscale Facilities featured themes to host speakers, presentations, and discussions surrounding artificial intelligence (AI) and cyberinfrastructure (CI) within the data lifecycle in facilities, cloud computing, and about societal and human intersections with AI.
Originally hosted January 12, 13, and 14, 2026
This project is supported by the U.S. National Science Foundation Office of Advanced Cyberinfrastructure in the Directorate for Computer Information Science under Grant #2127548.