Many manufacturing companies have experienced costly recalls and product failures because uncertainties in design, testing, and manufacturing processes were not adequately quantified. These failures have led to fatal accidents, billions of dollars in lost revenue, and even the collapse of major manufacturing firms. In response, industries such as aerospace, automotive, semiconductor, and medical devices have increasingly adopted Uncertainty Quantification (UQ)—a multidisciplinary framework drawing from statistics, applied mathematics, and engineering—to better design, test, and manufacture products under uncertainty.
This talk provides an overview of Uncertainty Quantification, explains why it has become indispensable in modern engineering, and introduces key design of experiment and predictive model methods for rigorously quantifying uncertainty in complex systems.
Peter Chien is a Professor of Statistics and Industrial & Systems Engineering at the University of Wisconsin–Madison and a Fellow of the American Statistical Association. He is the recipient of a National Science Foundation CAREER Award and an IBM Faculty Award. His research has been widely adopted by Fortune 500 companies across industries including aerospace, automotive, semiconductors, electronics, chemical, battery and life sciences.
This talk provides an overview of Uncertainty Quantification, explains why it has become indispensable in modern engineering, and introduces key design of experiment and predictive model methods for rigorously quantifying uncertainty in complex systems.
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