AI/ML Science & Technology
Interest Group
Building AI literacy for astronomical research through stackable, bite-sized modular training designed for the astronomy community.
About the STIG
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.
Established under the Cosmic Origins Program Analysis Group (COPAG), the STIG brings together researchers and educators to build a comprehensive AI education framework tailored for the astronomy community.
Program Schedule
A 23-week open-source lecture series. Click any row with aVideo & Materialsbadge to expand recordings, notebooks, and slides.
| Week | Date | Topic | Speaker |
|---|---|---|---|
| 1 | Nov 3 | OverviewVideo & Materials | Jesse Thaler, MIT |
| Module 1: Large Language Models as Autonomous Agents | |||
| 2 | Nov 10 | LLM API BasicsVideo & Materials | Yuan-Sen Ting, OSU |
| 3 | Nov 17 | RAG & Function ToolsVideo & Materials | Yuan-Sen Ting, OSU |
| 4 | Nov 24 | LLM as AgentVideo & Materials | Francisco Villaescusa-Navarro, Flatiron |
| 5 | Dec 8 | Model Context Protocol (MCP)Video & Materials | Yuan-Sen Ting, OSU |
| Module 2: Deep Learning Frameworks | |||
| 6 | Dec 15 | PyTorch and AutodifferentiationVideo & Materials | Phill Cargile, Harvard-Smithsonian |
| 7 | Dec 22 | JAXVideo & Materials | Phill Cargile, Harvard-Smithsonian |
| Module 3: Neural Network Basics | |||
| 8 | Jan 12 | Inductive BiasesVideo & Materials | John Wu, STScI |
| 9 | Jan 26 | Convolutional Neural Networks (CNNs)Video & Materials | John Wu, STScI |
| 10 | Feb 2 | Graph Neural Networks (GNNs)Video & Materials | Tri Nguyen, Northwestern |
| 11 | Feb 9 | TransformersVideo & Materials | Helen Qu, Flatiron |
| 12 | Feb 23 | Recurrent Neural Networks (RNNs)Video & Materials | Daniel Muthukrishna, Harvard/MIT |
| Module 4: Physics-Inspired Networks | |||
| 13 | Mar 2 | Equivariant Neural Networks (Theory) | Anna Scaife, U. of Manchester |
| 14 | Mar 9 | Equivariant Neural Networks (Application) | Anna Scaife, U. of Manchester |
| Module 5: Generative Models | |||
| 15 | Mar 16 | Normalizing Flows | Gregory Green, Westlake |
| 16 | Mar 23 | Diffusion Models | Duo Xu, U. of Toronto |
| 17 | Mar 30 | Flow Matching | Tomasz Rozanski, ANU |
| 18 | Apr 6 | Simulation-Based Inference | Tomasz Rozanski, ANU |
| Module 6: Ethics and Philosophy of Science | |||
| 19 | Apr 13 | Peer Review with AI: Promise and Pitfalls | Licia Verde, U. of Barcelona/JCAP |
| 20 | Apr 27 | The Meaning of Understanding in AI-Laden Science | Siyu Yao, STJU |
| Module 7: NSF Funding Opportunity | |||
| 21 | Apr 20 | NSF Funding Opportunity | Andrea Berlind, NSF |
| Module 8: Reinforcement Learning | |||
| 22 | May 4 | Reinforcement Learning Fundamentals | Carol Cuesta-Lazaro, IAS/Flatiron |
| 23 | May 11 | Reinforcement Learning Applications | Carol Cuesta-Lazaro, IAS/Flatiron |
Leadership Council
For inquiries, please contact: ting.74@osu.edu
How to Participate
Membership
The AI/ML STIG is open to the national and international community without regard to institutional affiliation, education, or career status. We welcome astronomers, astrophysicists, data scientists, and anyone interested in AI applications in astronomy.
Join Mailing ListRecordings
All lecture recordings are hosted on the NASA Cosmic Origins Program subpage, making them accessible to the broader community for asynchronous learning. Past lectures include YouTube recordings embedded directly in each lecture card above.
NASA Official Page