Speaker: Dr. Rui Tuo, Associate Professor, Department of Industrial & Systems Engineering, Texas A&M University

Faculty Hosts: Jian Tao, PVFA/TAMIDS

Abstract: This talk presents Gaussian process (GP) based modeling as a flexible and practical approach for learning unknown functions from data. The talk will explain the basic idea of placing a probability model on functions, enabling prediction together with uncertainty quantification. Key concepts such as kernels, prior and posterior distributions, and the role of training data will be introduced in an accessible way. Several motivating examples will be used to illustrate applications, including spatial prediction, computer experiments, engineering surrogate modeling, and optimization of expensive systems. Emphasis will be placed on intuition and interpretation. We will discuss the value of GP methods in real-world problems, as well as the limitations and challenges of the methods.

Biography: Rui Tuo is an Associate Professor in the Department of Industrial and Systems Engineering at Texas A&M University. He received his B.S. in Statistics from the University of Science and Technology of China and Ph.D. in Statistics from the University of Chinese Academy of Science. His research interest lies in data science for computer simulations, uncertainty quantification, non-parametric statistics, and probabilistic models for machine learning.