Kyle Gerard Felker is an Assistant Computational Scientist at Argonne National Laboratory in the Computational Science Division and is a member of the Catalyst team. Kyle currently works on applying deep learning to fusion energy science as a part of the Aurora Early Science Program. Previously, he held a postdoctoral appointment in the Leadership Computing Facility.
He received his Ph.D. in Applied and Computational Mathematics from Princeton University, where he worked on numerical methods for astrophysics and helped develop the Athena++ astrophysics code. Kyle was a Department of Energy Computational Science Graduate Fellow (CSGF) from 2014-2018. He holds a B.A. in Physics from the University of Chicago.
- Deep learning and artificial intelligence
- Fusion energy sciences
- Computational astrophysics
- High-performance computing
- Numerical analysis
- Reproducibility in computational science
- Performance portability