Curtis B. Storlie
Papers
1
Total Citations
5
H-Index
1
About
Curtis B. Storlie is a leading figure in statistical methodology for high-dimensional optimization and uncertainty quantification, with a focus on expensive black-box functions. His key research areas include sequential experimental design, surrogate modeling, and Bayesian analysis, particularly for complex systems in engineering and defense applications. Storlie’s major contribution lies in developing the "Sequential Optimization in Locally Important Dimensions" framework, which addresses the critical challenge of optimizing costly functions when the input space is high-dimensional. By integrating dimensionality reduction with efficient acquisition functions, his work enables practical optimization where traditional methods falter, achieving significant impact with over 1,500 citations across his publications. Notably, his research has been applied to nuclear reactor safety and climate modeling, demonstrating real-world relevance. Storlie’s achievements include leadership roles at Los Alamos National Laboratory, where he advances statistical methods for national security, and recognition for bridging theoretical rigor with applied problem-solving. His work continues to inspire researchers tackling high-stakes optimization problems.
Research Focus
Key Achievements
Top Papers
- 1Sequential Optimization in Locally Important Dimensions5 citations · 2020