*More details will be added to the schedule as they become available.*
For a complete list of conference talk titles and abstracts, click here.
For a complete list of conference poster titles and abstracts, click here.
Schedule for Thursday, September 4
| Time (CT) | Session | Location |
|---|---|---|
| 8:00–9:00 a.m. | Breakfast and registration | Kitchen-dining area |
| 9:00–10:30 a.m. | Generative AI for scientific data analysis and simulations 2 • Invited speaker: Fei Sha, Google Research—“Advances in Probabilistic Generative Modeling for Scientific Machine Learning” • Hong-Yu Chen, Northwestern University—“StarEmbed-GPT: a foundation model for general-purpose inference on variable stars” • Keiya Hirashima, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences—“Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback” • Tri Nguyen, Northwestern University—“Generating Halo Merger Trees with Graph Generative Models” | Auditorium and Zoom |
| 10:30–11:00 a.m. | Coffee break @ posters | SkAI Hub area |
| 11:00 a.m.–12:05 p.m. | Physics-informed architectures, interpretability, and ethics 2 • Peter Melchior, Princeton University—“Optimizing Instrument Utilization and Survey Designs with Structured Learning” • Amanda Wasserman, University of Illinois Urbana-Champaign—“Improving Supernova Cosmology with Active Learning Follow-up” • Aayush Saxena, University of Oxford—“Using Deep Learning to classify high-redshift galaxy spectra from JWST: uncovering exciting galaxy populations through AI” • Aritra Ghosh, University of Washington—“Harnessing ML & Large Surveys to Probe Galaxy Evolution: from HSC Size-Environment Correlations to Rubin Unsupervised Discovery” | Auditorium and Zoom |
| 12:05–12:30 p.m. | Poster lightning talks | Auditorium and Zoom |
| 12:30–2:00 p.m. | Mentoring lunch | Kitchen-dining area |
| 2:00–3:30 p.m. | Inference and uncertainty quantification for large and complex data 3 • Invited speaker: Matteo Sesia, University of Southern California—TBD • Alex Gagliano, Massachusetts Institute of Technology—“Mixture-of-Expert Variational Autoencoders for Multi-Modal Embedding of Supernova Data” • Liren Shan, Toyota Technological Institute at Chicago—“Volume Optimality in Conformal Prediction with Structured Prediction Sets” • Sreevani Jarugula, Fermilab—“Cosmology constraints from Strong Gravitational Lensing using Neural Ratio Estimation” | Auditorium and Zoom |
| 3:30–3:45 p.m. | Wrap-up: Conference photo | Auditorium and Zoom |
| 3:45–4:15 p.m. | Coffee break @ posters | SkAI Hub area |
| 4:15–5:15 p.m. | Poster session 2 | SkAI Hub area |
| 5:30–7:30 p.m. | Optional reception: Closing happy hour at CloudBar at 360 CHICAGO (top of the Hancock Center) | Top of Hancock Center |
The SkAI Institute is one of the National Artificial Intelligence Research Institutes funded by the U.S. National Science Foundation and Simons Foundation. Information on National AI Institutes is available at aiinstitutes.org.
