Research Seminar: "Statistical Modeling and Uncertainty Quantification" by Prof. Ying Hung

Spring 2020 SIP Seminar Series: April 29, 2020
[www.inspirelab.us/seminars/]
Speaker: Prof. Ying Hung
Title: Statistical Modeling and Uncertainty Quantification for Computer Simulations with Non-Gaussian Responses
Abstract: Non-Gaussian observations such as binary responses are common in some computer simulations, but most of the work in the literature is limited to the analysis of continuous responses. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with Gaussian process models. We also propose a new calibration framework for binary responses. Its application to the T cell adhesion data provides insight into the unknown values of the kinetic parameters which are difficult to determine by physical experiments due to the limitation of the existing experimental techniques.
Biography: Dr. Hung is an Associate Professor in Statistics Department at Rutgers. She graduated from Industrial and Systems Engineering at Georgia Institute of Technology in 2008. Dr. Hung received NSF CAREER award and IMS Tweedie Award in 2014. Her research areas include experimental design, statistical modeling for computer experiments and uncertainty quantification, with applications to science and engineering.