Special Session SS33  24 June 2019

Learning the Milky Way: Artificial Intelligence Applications from Solar System to Galaxy Scales

Aims and scope

Machine learning is a sub-field of computer science in which algorithms learn and evolve without explicit programming. Machine learning algorithms can mechanize the process of source identification, scale efficiently to large datasets, and produce repeatable catalogs. Outside astronomy it is commonly applied to pattern recognition problems, including topics ranging from genome sequencing to face recognition to drug discovery.

This session is inspired by the growing adoption of machine learning approaches in the astronomy community. We aim to bring together researchers applying machine learning techniques to data intensive problems in the fields of exoplanets, stars, the interstellar medium and galaxies. The goal is to discuss and share new approaches, disseminate recent results and promote the application of existing algorithms to new problems.


Invited speakers

  • Dario Colombo (MPIFR)
  • Claire Murray (StScI)
  • Yuan-Sen Ting (Princeton)
  • Jonathan Holdship (UCL)
  • Ben Moster (MPIA)

Scientific organisers

  • Stella Offner (University of Texas)
  • Serena Viti (University College London)
  • Ralf Klessen (University of Heidelberg)
  • Stefanie Walch (University of Cologne)
  • Thorsten Naab (MPA)
  • Ullrich Koethe (University of Heidelberg)
  • John Wenskovitch (Virginia Tech)
  • Amruta Jaodand (ASTRON)


Stella Offner: soffner [AT] astro.as.utexas.edu

Updated on Tue Feb 05 15:55:02 CET 2019