This course is intended to provide students with a comprehensive overview of the rapidly-changing interface of glaciology with machine learning and artificial intelligence. Key topics that may be covered include

  • An overview of machine learning methods for glaciologists
  • Adjoint models and backpropagation
  • Deep learning for glaciological remote sensing
  • Gaussian process emulation for model prediction
  • Deep learning-based emulation of ice physics

The course is primarily oriented towards PhD students and early-stage postdocs that have strong foundational knowledge in glaciology, glaciological modeling, or remote sensing, and that wish to integrate ML methods into their work while simultaneously working to establish a community of practice.

The course will be held from June 16th-24th and will include 3 days of interactive lectures by course instructors, 3 days of student project development in collaboration with course instructors, a one day excursion to nearby Yellowstone National Park, and two days for travel and orientation. Instructors include

  • Doug Brinkerhoff, University of Montana
  • Ching-Yao Lai, Stanford University
  • Mauro Perego, Sandia National Laboratory
  • Daniel Cheng, NASA Jet Propulsion Laboratory
  • Jacob Downs, University of Montana

Fixed costs for attending the course (including airfare, transportation, and room and board at the Taft-Nicholson Center) are covered by a generous grant from the National Science Foundation.


32 Campus Drive
Missoula, Montana 59812