Empirical models that relate multiple quality features to a set of design variables play a vital role in many industrial process optimization methods. Many of the current modeling methods employ a single-response model to analyze industrial processes without taking into consideration the high correlations among the response variables and may result in a misleading prediction model, and therefore, poor process design.

Speaker Bio: Min Wang is an Associate Professor of Statistics in the Department of Management Science and Statistics at The University of Texas at San Antonio. He previously worked at Michigan Technological University and Texas Tech University. His main research spans the areas of Bayesian inference and methods, high-dimensional inference, prior elicitation, quantile regression, and statistical modeling, all in both methodological and theoretical perspectives. His work has been appeared or accepted in top-ranked journals, such as Bayesian Analysis, Bernoulli, Computers & Industrial Engineering, IISE Transactions, International Journal of Production Research, Journal of the Operational Research Society, Journal of Statistical Planning and Inference, Naval Research Logistics, The American Statistician, and others.

Location: Executive Conference Room, BB 4.02.10

Abstract and Bio (PDF)