Prof Shoemaker obtained her Ph.D. in mathematics from the University of Southern California supervised by Richard Bellman in Dynamic Programming.

[1] Prof. Shoemaker's research focuses on finding cost-effective, robust solutions for engineering problems by using computational mathematics for optimization, modeling, deep learning and statistical analyses.

The surrogates are iteratively built during the search process and with intelligent algorithms that effectively utilize computing distributed over parallel processors.

The optimization and uncertainty quantification effort is used to improve model forecasts, to evaluate monitoring schemes and to have a tool for comparing alternative management practices.

"Weighted Nonlinear Feedback for Optimal Control under Uncertainty with Application to Groundwater Remediation,"[5] by Whiffen and Shoemaker (U.S. Patent 5,468,088) 2.

(This patent generates royalties and is based on paper in prestigious computer architecture conference: Petrica, P., A. Izaelevitz, D.H. Albonesi, C.A.