Two immersive days exploring how large foundation models, transformers, diffusion models, self-supervised learners can accelerate astronomical discovery. Experts from gravitational-wave astronomy, multimessenger astronomy, and AI will share insights through talks and panel discussions.
Multimessenger astronomy is entering a transformative era. As next-generation observatories deliver an unprecedented volume and diversity of data—across gravitational waves, electromagnetic signals, and neutrinos—the complexity of extracting meaningful insights increasingly exceeds the capacity of traditional analysis pipelines. At the same time, foundational AI models—large pre-trained networks such as transformers and diffusion models—are reshaping the landscape of scientific discovery, from natural language processing to molecular design. This two-day workshop brings these revolutions together. We will convene researchers from gravitational-wave physics, broader observational astronomy, and artificial intelligence to explore how cutting-edge machine learning can accelerate real-time detection, multimodal data integration, source classification, and astrophysical inference.
Through keynote talks and interactive panel discussions, participants will:
- Survey the current frontier of AI-driven multimessenger astronomy—what tools are proving effective, and where the next breakthroughs may arise.
- Build collaborative bridges across observatories, academic institutions, and industry research groups to foster robust, open-source development.
- Chart a roadmap toward interpretable, scalable AI systems that can adapt to rapidly evolving data streams and scientific goals.
Join us in Nashville, August 4–5, 2025, to shape the future of multimessenger astronomy in the era of foundational AI—and to help lay the groundwork for a new generation of discovery.
Organizing Committee
Prof. Karan Jani, Department of Physics and Astronomy
Prof. Jesse Spencer-Smith, Data Science Institute
Prof. Stephen Taylor, Department of Physics and Astronomy
Dr. Chayan Chatterjee, Department of Physics and Astronomy, Data Science Institute
Dr. Abigail Petulante, Data Science Institute