Morning e-Poster	09:45-10:30
Morning Oral Session	10:30-12:00
IAUS 368-1
Sara Webb	ML tutorial for the broader community
Afternoon Oral Session 1	13:30-15:00
IAUS 368-2
Guillermo Cabrera	Classic Machine Learning vs Deep Learning: when, why and how?
Annalisa Pillepich	ERGO-ML: Extracting Reality from Galaxy Observables with Machine Learning
Break	15:00-15:15
Afternoon Oral Session 2	15:15-16:45
IAUS 368-3
Michelle Lochner	Machine Learning in Astronomy
Panel Discussion	Broader ML Topics

Morning Plenary	08:15-09:45
George Djorgovski	Machine Learning in Astronomy: From the Star-Galaxy Separation to a Collaborative Human-AI Discovery
Ofer Lahav	Deep Learning in Astronomy: Trends and Challenges
Morning e-Poster	09:45-10:30
Morning Oral Session	10:30-12:00
IAUS 368-4
Renee Hlozek	Existing data sets for machine learning in Astronomy
Panel Discussion
Afternoon Oral Session 1	13:30-15:00
IAUS 368-5
David Parkinson	Detecting complex sources in large surveys using an apparent complexity measure
Dennis Crake	In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe.
Didier Fraix-Burnet	Unsupervised classification: a necessary step for Deep Learning?
Gordian Edenhofer	Iterative Grid Refinement: Approximate Gaussian Processes for Billions of Parameters
Jeroen Auderaert	Unraveling the physical mechanisms of pulsating stars through a multimodal and multidisciplinary machine learning approach
Lukasz Wyrzykowski	Time-domain photometry and machine learning with OGLE and Gaia
Break	15:00-15:15
Afternoon Oral Session 2	15:15-16:45
IAUS 368-6
Panel Discussion	Practical Problem Solving - including interpretability
Afternoon e-Poster	16:45-17:30

Morning e-Poster	09:45-10:30
Morning Oral Session	10:30-12:00
IAUS 368-7
Eric Ford	Enhancing Exoplanet Surveys via Physics-informed Machine Learning
Panel Discussion	GW/MMA
Afternoon Oral Session 1	13:30-15:00
IAUS 368-8
Ivy Wong	A review of current tools for outreach & education
Melissa Lopez	Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
Mike Walmsley	Galaxy Zoo: Practical Methods for Large-Scale Learning
Joshua Speagle	Incorporating Errors in Machine Learning Methods
Break
Afternoon Oral Session 2	15:15-16:45
IAUS 368-9
Raquel Ruiz Valenca	Comparing machine learning and deep learning models to estimate quasar photometric redshifts
Steffani Grondin	Searching for the extra-tidal stars of Galactic globular clusters with high-dimensional clustering analysis
Vishal Upendran	Accelerating astronomy workflow with deep learning and interpretable A.I
Yuan-Sen Ting	Quantifying non-Gaussianity with mathematical insights from machine learning
SOC	Meeting Summary and Next Steps
SOC	Closing Remarks
Afternoon e-Poster	16:45-17:30