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.
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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
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
An introduction to equivariant neural networks, exploring the theoretical foundations of how symmetry constraints can be built directly into neural network architectures for more efficient and physically meaningful learning.
Build Recurrent Neural Networks from first principles and apply them to real-time classification of astronomical transients. Implement vanilla RNNs, LSTMs, and GRUs in PyTorch and train a classifier on supernova light curves. Topics Covered Meeting Connection Join the Meeting
Build a decoder-only transformer (a small GPT-like language model) from scratch in PyTorch. Train it on the Tiny Shakespeare dataset for character-level language modeling and use it to generate text, understanding every component along the way.
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