Science and Technology Interest Group
Artificial Intelligence and Machine Learning Science and Technology Interest Group
The NASA Cosmic Origins Program AI/ML Science and Technology Interest Group (AI/ML STIG) addresses the critical need to upskill the astronomy community with AI literacy. We provide structured, domain-specific AI education through stackable, bite-sized modular training designed for astronomical research contexts.
About AI/ML STIG
Building AI Literacy for Astronomical Research
Astrophysics is an emerging technology for big-data science, and the use of Artificial Intelligence (AI) and Machine Learning (ML) technology will be inevitable in the coming decades.
The AI and ML Science and Technology Interest Group (AI/ML STIG) is motivated by the awareness that upskilling the scientific community could have a transformative impact to counter critical challenges facing astronomical research today.
By providing structured, domain-specific AI education, the AI/ML STIG aims to accelerate NASA's competitive advantage in AI-enabled space science, build the interdisciplinary workforce essential for next-generation astronomical discoveries, create a model for other NASA programs facing similar upskilling challenges, and establish NASA’s leadership in responsible AI adoption to maximize the science return from its missions by the community. The modular, community-driven approach ensures scalability while maintaining the rigor and domain relevance essential for meaningful scientific advancement. This STIG serves as a focal point for addressing these challenges through community townhalls for discussions and organizing short tutorials to address specific astronomical AI applications, modules, and foundational concepts.
Subscribe to the AI/ML STIG Email List
To join the list, send an email to AI-ML-STIG-join@lists.nasa.gov with Subject="join"
To unsubscribe, send an email to AI-ML-STIG-leave@lists.nasa.gov with Subject="leave"
Your email address will be used only for the purpose of subscription to the selected email distribution list. For further information, read the NASA Web Privacy Policy.
STIG Leadership
| Yuan-Sen Ting | OSU |
| Alex Gagliano | MIT |
| Siddharth Mishra-Sharma | Boston University |
| Digvijay Wadekar | Johns Hopkins |
| Andrew Saydjari | Princeton |
| Carol Cuesta-Lazaro | MIT |
| Georgios Valogiannis | UChicago |
News & Events
Meetings, conferences, seminars, workshops, and other news and events for the STIG
AI and Scientific Publishing. Speaker: Licia Verde, U. of Barcelona/JCAP
Simulation-Based Inference Speaker Tomasz Rozanski, ANU An introduction to simulation-based inference (SBI) for astronomy, using normalizing flows to perform posterior estimation in settings where the likelihood is intractable. Hands-on tutorials cover a toy physics problem and a stellar population application.…
Flow Matching Speaker Tomasz Rozanski, ANU A hands-on tutorial on flow matching for generative modeling in astronomy, covering continuous normalizing flows applied to stellar population modeling with both single and conditional population examples. Topics Covered Meeting Connection Join the Meeting
Diffusion Models. Speaker Duo Xu, U. of Toronto
Normalizing Flows Speaker Gregory Green, Westlake An introduction to normalizing flows for generative modeling, covering the mathematical foundations of invertible transformations and density estimation, with hands-on tutorials implementing RealNVP and flow matching techniques. Topics Covered Meeting Connection Join the Meeting
Equivariant Neural Networks (Application) Speaker: Anna Scaife, U. of Manchester
News Straight to Your Inbox
Subscribe to your community email news list
We will never share your email address.
