About
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 14th-23rd and will include 4 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. Previous 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.