NSF-Simons AI Institute for Cosmic Origins
Funded by the U.S. National Science Foundation and the Simons Foundation. Driving AI innovation to explore the cosmos
CosmicAI develops transformative AI methods to meet pressing astronomical challenges and tackle outstanding questions about our cosmic origins. Research spans four fundamental AI themes: trustworthiness, efficiency, interpretability, and robustness.
CosmicAI aims to serve as a nexus of collaboration to increase the accessibility of astronomy and AI data and methods through open-source AI-powered tools, data sharing, and AI educational initiatives.
Towards AI-driven Radio Image Reconstruction with Omkar Bait
Light Scattering of Irregular Grains with Neural Networks with Zhé-Yǔ Daniel Lín
Machine Learning for Reviewer-Proposal Matching in ALMA Distributed Peer Review
Compound AI Systems: How Publisher AI Helps Researchers
Computer Vision for Scientific Discovery
Accelerating (Astro)chemical discovery with machine learned atomistic models
CosmicAI Institute Tackles Universe’s Deepest Mysteries - Interview with Dr. Stella Offner
Time-Series Modeling of High-Resolution Radio Spectra - Josh Taylor (Oden Institute)
Finding Exotic Transients in the Era of Big Data with Sebastian Gomez (UT Austin)
Strategies for variance reduction in spectral unmixing - Jordan Bryan (UVA)
Encoding of Spectra and Time Series - Peter Melchior (Princeton University)
Imaging in Radio Interferometry - What do we measure, model and make decisions about? - Urvashi Rau
How AI is Transforming Astronomy - Dr. Stella Offner at the AI+Science Expo
Lina Necib - (Machine) Learning of Dark Matter
Exploring the LLM universe for astronomy research - Dr. Jessy Li and Sebastian Joseph
Transcending the Limits of Astrostatistics with Machine Learning Methods - Yuan-Sen Ting
Six Months of CosmicAI: A Look Back and Ahead with CosmicAI PI and Director, Dr. Stella Offner
Structure preserving, Low Parameter, Interpretable, Operator Learning - Varun Shankar
Science with massive radio datasets enabled using AI/ML - Dr. Mark Lacy
Cosmological Emulators for High Dimensional Inference - Dr Mahdi Qezlou
Learning how Stars Form: Harnessing AI to Identify Structures in Noisy Spectral Cubes
Year of AI - Galactic Quest at CosmicAI Institute