*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 Friday, September 5
| Time (CT) | Session | Location |
|---|---|---|
| 8:00–9:00 a.m. | Breakfast and registration | Kitchen-dining area |
| 9:00–10:35 a.m. | Physics-informed architectures, interpretability, and ethics 3 • Helen Qu, Flatiron Institute—“An Astrophysical Case Study in Robustness” • Akraprabha Ganguli, Argonne National Laboratory—“Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets” • Adrian Bayer, Flatiron Institute/Princeton University—“Optimal, Interpretable, and Robust Cosmological Inference of the Multimodal Sky” • Daniel Anglés-Alcázar, University of Connecticut—“Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to model the impact of baryonic physics on cosmological structure formation” • Nolan Smyth, University of Montreal—“Towards the automated discovery of differential equations in (astro)-physics” • Jack O'Brien, University of Illinois Urbana-Champaign—“Physics Informed Latent Space in Foundation Models of Transients” | Auditorium and Zoom |
| 10:35–11:00 a.m. | Coffee break @ posters | SkAI Hub area |
| 11:00 a.m.–12:35 p.m. | Inference and uncertainty quantification for large and complex data 4 • Jimena González, University of Wisconsin–Madison—“Does Machine Learning Work? A Comparative Analysis of Strong Gravitational Lens Searches in the Dark Energy Survey” • Anirban Bairagi, Institut d'Astrophysique de Paris—“PatchNet: GPU and Simulation is not the limitation anymore for Cosmological field-level inference” • Yuanyuan Zhang, NSF NOIRLab—“Lessons Learned from a Simulation-Based Inference Approach for Galaxy Cluster Abundance Cosmological Analysis” • Samuel Dyson, The University of Chicago—“Uncertainty Quantification in Time-Series Coincidence Detections” • Lindsay House, SkAI Institute—“Harnessing AI and Citizen Science to Improve Our Understanding of Dark Energy” • Ana Sofia Uzsoy, Harvard University—“Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting” | Auditorium and Zoom |
| 12:35–1:00 p.m. | Closing: Wrap-up, Discovery Engine, synthesis | Auditorium and Zoom |
| 1:00–2:00 p.m. | Lunch | Kitchen-dining area |
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.
