Star Formation

across cosmic scales:

Machine Learning

insights and applications

13-17 May 2024, Budapest, Hungary

Important dates!

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.

Invited Speaker:

  • Tristan Cantat-Gaudin (Max Planck Institute for Astronomy, Heidelberg, Germany) - remote
  • Marina Kounkel (University of North Florida, Jacksonville, FL, USA)
  • Ann Marie Cody (SETI Institute, Mountain View, CA, USA) - remote
  • Emily L. Hunt (Universität Heidelberg, Heidelberg, Germany)
  • Michael A. Kuhn (University of Hertfordshire, Hatfield, UK)
  • Laura Venuti (SETI Institute, Mountain View, CA, USA)
  • András Kovács (HUN-REN Konkoly Observatory, Budapest, Hungary)
  • Nikos Gianniotis (Heidelberg Institute for Theoretical Studies, Heidelberg, Germany)
  • more to be announced

  • Young star populations, galactic structure
  • Star- and planet formation
  • YSO variability, time domain analysis
  • Machine learning techniques, classification and regression type problems
  • Surveys, data types
  • Link with extragalactic surveys
Scientific Organising Committee:

  • Rosaria Bonito (INAF - Osservatorio Astronomico di Palermo)
  • Sotiria Fotopoulou (University of Bristol)
  • Ágnes Kóspál (Konkoly Observatory)
  • João Alves (University of Vienna)
  • Ashish Mahabal (California Institute of Technology)
  • Odysseas Dionatos (University of Vienna)
  • Marc Audard (University of Geneva)
  • Gábor Marton (Konkoly Observatory)

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