Check out the 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