Update: Abstract acceptance decisions sent on Nov 23

Check out the preliminary programme

Workshop Date: Sunday 31 January 2021

The widespread availability of machine learning (ML) technologies promises to disrupt scientific disciplines. Popular open source ML frameworks are not only useful for data-driven model fitting, but also for efficient computation of physics-based models. This COSPAR 2021 cross-disciplinary workshop is dedicated to showcasing use cases of ML technologies to observational and simulation data. This includes applications to:

  • satellite imagery classification and image restoration (including super-resolution),
  • space weather prediction,
  • exoplanet detection and characterization,
  • astrophysical simulations,
  • data augmentation, and
  • compressed sensing and inverse problems.

The workshop will feature invited talks, contributed talks, poster presentation as well as a panel discussion. For abstract submission, click here.

Invited speakers

  • Madhulika Guhathakurta (NASA HQ) - Machine Learning & Space Science at Frontier Development Lab
  • Shirley Ho (Flatiron Institute) - Machine Learning for Astrophysical Simulations

Technical Organizing Committee

  • Mark Cheung, Lockheed Martin Advanced Technology Center, Palo Alto, CA, USA
  • James Parr, NASA Frontier Development Lab (FDL) & FDL Europe
  • Bill Diamond, SETI Institute, Mountain View, CA, USA
  • Andrés Muñoz-Jaramillo, Southwest Research Institute, Boulder, CO, USA
  • Massimo Mascaro, Google Cloud, Mountain View, CA, USA
  • Atılım Güneş Baydin, University of Oxford, UK
  • Rajat Thomas, University of Amsterdam, NL

COSPAR 2021 Anchor Sponsor: Lockheed Martin