Date: Sunday 31 January 2021

Time: 9:30am – 6:00pm

Room: Cockle Bay Room, Level 2, International Convention Center Sydney

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