SPEAKER: Lulu Kang
PhD Candidate  (in the Statistics Program)
School of Industrial and Systems Engineering
Georgia Tech

ABSTRACT:

Computer experiments simulate the engineering systems by implementing the mathematical models governing the systems in computers. Recently, experiments having large number of input variables and experimental runs started to emerge. In the existing literature, kriging has been commonly used for approximating the complex computer models, but it has limitations for dealing with the large-scale experiments due to its computational complexity and numerical stability. In this work, I propose a new modeling approach known as regression-based inverse distance weighting (RIDW). The new predictor is shown to be computationally more efficient than kriging while producing comparable prediction performance. We also develop a heuristic method for constructing confidence intervals for prediction. I will also discuss extensions of RIDW and my future research directions on this exciting topic
Bio:
Lulu Kang is a Ph.D. candidate in the Statistics Program of the School of Industrial and Systems Engineering at Georgia Institute of Technology. She is working with Professor Roshan J. Vengazhiyil. Her research interests are in developing statistical theories and methodologies, as well as their applications in physical science and engineering.