This workshop aims to bring together researchers interested in automatic differentiation methods, tools and frameworks, and practitioners who need derivatives and gradients for parallel or HPC workloads, in application areas spanning applied mathematics, scientific computing, computational engineering, and machine learning. The workshop features invited talks from both the framework developer and user communities, and is soliciting extended abstracts for contributed talks on topics including, but not limited to

  • Automatic differentiation tool development
  • Model, theory, and method development for the differentiation of computer programs
  • Differentiable languages, or domain-specific languages or frameworks that support differentiation or gradient computations
  • Case studies and experiences of computing derivatives of parallel or large-scale computations, or of trying to scale differentiable applications
  • Approaches closely related to differentiation, which may include aspects of e.g. probabilistic programming, uncertainty quantification, or error estimation

Important Dates

  • Abstract Submission: December 8, 23:59 AOE
  • Notification: January 8, 23:59 AOE
  • Workshop during: 2nd of March

Invited Speakers



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