Auralee Edelen (Colorado State University, Fermilab)

My work is centered on improving modeling and control methods for particle accelerator systems, primarily using neural networks. I've mainly worked on learning models of accelerator dynamics (both from simulation and measured data), learning control policies, and combining approaches like MPC with neural networks and reinforcement learning. I was an early adopter for my field (2012) and since then have also been trying to increase general engagement of the particle accelerator community with machine learning.