Research Interests:

• Machine learning
• Nonlinear/complex system dynamics
• Optimization methods
• Control theory
• Beam dynamics
• Accelerator physics

Auralee earned her undergraduate degree in Physics from Rensselaer Polytechnic Institute with magna cum laude honors. While at RPI, she also earned minors in Philosophy of Science and Mathematics; Psychology; and Science, Technology, and Society. Auralee also conducted part-time graduate work at Johns Hopkins University while working full-time as a researcher in Washington, D.C.

Auralee completed several research internships in different fields during her undergraduate education, including two National Science Foundation Research Experience for Undergraduates internships (NSF REUs) and one DOE Science Undergraduate Laboratory Internships (SULI) program at Los Alamos National Lab. Her first internship focused on the observation and analysis of optical magnitude variability in quasars at Colgate University, where she had previously taken courses while still a high school student. Her second internship was completed within the Institute for Research in Electronics and Applied Physics at the University of Maryland, where she studied shear-induced birefringence in aqueous polymer solutions. In her third internship, she worked with the N-1 microcalorimetry group at Los Alamos studying particle detection with transition edge sensing microcalorimeters.

After graduating from RPI, Auralee worked as a research engineer based in the Theory, Modelling, and Analysis Branch of the Naval Surface Warfare Center, Carderock Division (where she was an on-site contractor). While there, she worked on an effort to develop and validate boundary element modelling approaches for underwater electromagnetic signature prediction. This included the design/analysis of numerical models and the design/analysis of experimental tests for model validation, as well as participation in larger-scale physical model tests with the Naval Research Laboratory and international collaborators.

Her graduate studies are focused on improving control systems for particle accelerators through the use of online optimization, model predictive control, neural network models, and neural network control policies. She has also been involved in the build-up of the CSU linac.