Extended abstract submission deadline: March 22.
Reminder: early registration deadline is April 10.
In recent years, the astronomical community has witnessed an unprecedented surge in data volume due to the advent of large-scale sky surveys and advanced observational facilities, providing high resolution observations in imaging, spectroscopy and time domain. Machine learning provides a powerful toolkit for extracting meaningful insights from these massive datasets, enabling us to identify complex patterns, model physical relationships, and predict properties of our targets. The SFML2024 conference, organised by the H2020 funded NEMESIS (Novel Evolutionary Model for the Early Stages of Stars with Intelligent Systems) consortium, will explore the diverse applications of machine learning from identifying young stellar candidates in large-scale surveys to understanding the impact of various factors on the birth and evolution of stellar systems. By bringing together experts in star formation and machine learning, and extending the invitation to extragalactic experts, our goal is to foster collaborative discussions and showcase groundbreaking research that leverages machine learning to unravel the mysteries of star formation across cosmic scales.