Our research group applies machine learning methods to several topics in astrophysics, such as:

  • Equivariant convolutional neural networks for investigating galaxy clusters
  • Diffusion models for conditional generation of galaxy images
  • Graph neural networks representing large scale structure
  • Exploiting machine learning for maximizing science with wide-area imaging surveys
  • Best practices and ethics for astronomical and scientific machine learning
  • Strategic use of artificial intelligence to improve research astronomy