Conference Program
The Open SkAI Conference will take place from Tuesday, September 2–Friday, September 5, 2025. It will be located in downtown Chicago, IL at the SkAI Hub.
*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.
Tuesday, September 2
| Time (CT) | Session | Location |
|---|---|---|
| 8:00–9:30 a.m. | Breakfast and registration (for tutorial participants and volunteers) | Reception/lobby |
| 9:30–10:30 a.m. | Tutorials (presented by Sam Foreman and Sandeep Madireddy from Argonne National Laboratory) —“Scientific AI at Scale: Architectures and Distributed Training”—AI for science is accelerating as a data-and-model revolution takes hold, with advances in deep learning and foundation models showing that scale unlocks discovery, while translating those gains into scientific value still requires domain-specific design and customization. This tutorial will survey the major modeling architectures and learning approaches used for scientific datasets and tasks, and cover scalable training strategies, including data, model, and pipeline parallelism and distributed I/O, enabling efficient training on leadership-class systems at the Argonne Leadership Computing Facility (ALCF). | SkAI Main Conference Room |
| 10:30–11:00 a.m. | Coffee break & poster set up | |
| 11:00 a.m.–12:00 p.m. | Tutorials—“Scientific AI at Scale: Architectures and Distributed Training” (cont.) | SkAI Main Conference Room |
| 12:00–1:00 p.m. | Lunch (for tutorial participants) | Kitchen-dining area |
| 1:00–1:30 p.m. | Opening • Vicky Kalogera, Northwestern University—Welcome and introduction • Elise Ahn, Northwestern University—Welcome and housecleaning • R. Chris Smith, Interim Division Director, NSF—Opening remarks • Alex Ji, The University of Chicago—Introduction of glossary | Auditorium and Zoom |
| 1:30–2:30 p.m. | Plenary 1—Session chair: Gautham Narayan, University of Illinois Urbana-Champaign • Federica Bianco, University of Delaware—“Do Androids Dream of Exploding Stars?” | Auditorium and Zoom |
| 2:30–3:00 p.m. | Poster lightning talks | Auditorium and Zoom |
| 3:00–4:00 p.m. | Coffee break @ posters | SkAI Hub area |
| 4:00–5:20 p.m. | Inference and uncertainty quantification for large and complex data 1—Session chair: Emma Alexander, Northwestern University • Ved Shah, Northwestern University—“ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST” • Hyosun Park, Yonsei University—“Transformer-based Reduction of PSF Effect and Correlated Noise for Precision Dark Matter Mapping” • Georgios Valogiannis, The University of Chicago—“Saturating Cosmological Information with AI: Field-Level Inference and Beyond” • Shubhendu Trivedi, Fermilab—“Conformal Hierarchical Simulation-based Inference with Local Validity” • Patricia Iglesias-Navarro, Institute of Astrophysics of the Canary Islands—“Simulation-based inference of galaxy properties from JWST pixels” | Auditorium and Zoom |
| 5:20–5:30 p.m. | Day 1 wrap-up: Introduction to the SkAI community Exploration of Emerging Research (SEER) program, Astro-AI glossary, community norms | Auditorium and Zoom |
Wednesday, September 3
| Time (CT) | Session | Location |
|---|---|---|
| 8:00–9:00 a.m. | Breakfast and registration | Kitchen-dining area |
| 9:00–10:00 a.m. | Plenary 2—Session chair: Aggelos Katsaggelos, Northwestern University • George Karniadakis, Brown University—“From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?” | Auditorium and Zoom |
| 10:00–10:30 a.m. | Coffee break @ posters | SkAI Hub area |
| 10:30 a.m.–12:00 p.m. | Physics-informed architectures, interpretability, and ethics 1—Session chair: Lu Cheng, University of Illinois Chicago • Invited speaker: Chenhao Tan, The University of Chicago—“Structured Creativity in Science: AI for Hypothesis Generation and Research Ideation” • Manuel Ballester Matito, Northwestern University—“Accelerating Stellar Structure Modeling with Neural PDE Solvers” • Matt Ho, Columbia University—“Learning the Universe: Building a Scalable, Verifiable Emulation Pipeline for Astronomical Survey Science” • Laura Trouille, Adler Planetarium, Zooniverse—“Zooniverse and SkAI: Human-AI Collaboration for Scalable Scientific Discovery” | Auditorium and Zoom |
| 12:00–1:30 p.m. | Lunch/mentoring time | Kitchen-dining area |
| 1:30–2:50 p.m. | Generative AI for scientific data analysis and simulations 1—Session chair: Sandeep Madireddy, Argonne National Laboratory • Jiezhong Wu, Northwestern University—“A foundation AI model to infer the physics of transients” • Shunyuan Mao, Rice University—“Multi-resolution neural representation for self-supervised image reconstruction in radio interferometry” • Bin Xia, Georgia Tech and Argonne National Laboratory—“Towards a Generalizable Multi-Modal Foundation Model for Astrophysical Data” • Supranta Boruah, University of Pennsylvania—“Generative machine learning solutions for weak lensing mass mapping” • Tianao Li, Northwestern University—“Probabilistic Imaging of Galaxies for Weak Gravitational Lensing” | Auditorium and Zoom |
| 2:50–3:15 p.m. | Coffee break @ posters | SkAI Hub area |
| 3:15–4:15 p.m. | Poster session 1 | SkAI Hub area |
| 4:15–5:15 p.m. | Inference and uncertainty quantification for large and complex data 2—Session chair: Chihway Chang, The University of Chicago • Invited speaker: Rachel Mandelbaum, Carnegie Mellon University—“Galaxy Brain: AI Approaches to Large Sky Surveys” • Matiwos Mebratu, Stanford University—“Hybrid Prior Wavelet based Conditional Flow Matching Model (HyWave-CFM)” | Auditorium and Zoom |
| 5:15–5:30 p.m. | Day 2 wrap-up: Updates for the SkAI community Exploration of Emerging Research (SEER) program, Astro-AI glossary | Auditorium and Zoom |
Thursday, September 4
| Time (CT) | Session | Location |
|---|---|---|
| 8:00–9:00 a.m. | Breakfast and registration | Kitchen-dining area |
| 9:00–10:35 a.m. | Generative AI for scientific data analysis and simulations 2—Session chair: Aravindan Vijayaraghavan, Northwestern University • 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 |
| 10:35–11:00 a.m. | Coffee break @ posters | SkAI Hub area |
| 11:00 a.m.–12:05 p.m. | Physics-informed architectures, interpretability, and ethics 2—Session chair: Harley Katz, The University of Chicago • Peter Melchior, Princeton University—“Optimizing Instrument Utilization and Survey Designs with Structured Learning” • Helen Qu, Flatiron Institute—“An Astrophysical Case Study in Robustness” • Aayush Saxena, University of Oxford—“Using Deep Learning to classify high-redshift galaxy spectra from JWST: uncovering exciting galaxy populations through AI” • Arkaprabha Ganguli, Argonne National Laboratory—“Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets” | 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 |
| 2:00–3:35 p.m. | Inference and uncertainty quantification for large and complex data 3—Session chair: Rebecca Willett, The University of Chicago • Invited speaker: Matteo Sesia, University of Southern California—“Conformal Inference for Open-Set and Imbalanced Classification” • Yunyi Shen, 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 |
| 3:35–3:45 p.m. | Wrap-up: Conference photo | |
| 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 |
Friday, September 5
| Time (CT) | Session | Location |
|---|---|---|
| 8:00–9:00 a.m. | Breakfast and registration | Kitchen-dining area |
| 9:00–10:25 a.m. | Physics-informed architectures, interpretability, and ethics 3—Session chair: Aleksandra Ćiprijanović, Fermilab • Jack O'Brien, University of Illinois Urbana-Champaign—“Physics Informed Latent Space in Foundation Models of Transients” • Amanda Wasserman, University of Illinois Urbana-Champaign—“Improving Supernova Cosmology with Active Learning Follow-up” • 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” • 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 |
| 10:25–11:00 a.m. | Coffee break @ posters | SkAI Hub area |
| 11:00 a.m.–12:20 p.m. | Inference and uncertainty quantification for large and complex data 4—Session chair: Ermin Wei, Northwestern University • 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” • Ana Sofia Uzsoy, Harvard University—“Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting” | Auditorium and Zoom |
| 12:20–12:45 p.m. | Closing: SkAI community Exploration of Emerging Research (SEER) program and Conference Closing | Auditorium and Zoom |
| 12:45–1:45 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.
