Prof Jeff Wu from Georgia Institute of Technology presents the major developments from physical to virtual experimental designs. He reviews some key principles as well as provides a fresh look at interactions and effect aliasing with some new insights on an age-old problem.
Free and open to the public. Seating is on a first-come first-served basis.
Experimental design has been a scientific discipline since the founding work of Fisher. During the 80-year history, its development has been largely dominated by work in physical experiments. With advances in high-performance computing and numerical modeling, virtual experiments on a computer have become viable. This talk will highlight some major developments (physical and virtual) in this long period. Fisher’s principles (replication, randomization, blocking) will be reviewed, together with principles (effect hierarchy, sparsity, heredity) for factorial experiments. A fresh look at interactions and effect aliasing will be provided, with some surprisingly new insights on an age-old problem. Robust parameter design, another significant development which focuses on variation modeling and reduction, will be mentioned. Turning to computer experiments, the key differences with physical experiments will be highlighted. These include the lack of replication errors which entails new governing principles other than Fisher’s and the use of space-filling designs instead of fractional factorials. There are two strategies for modeling and analysis: based on Gaussian processes or on function approximations. These seemingly conflicting approaches can be better linked by bringing a stochastic structure to the numerical errors. Throughout the talk, real experiments/data, ranging from manufacturing to nano technology, will be used for illustration.
About the speaker
Jeff Wu is a Professor in Industrial and Systems Engineering at Georgia Institute of Technology, where he holds the Coca-Cola Chair in Engineering Statistics. He was formerly the H. C. Carver Professor of Statistics and Professor of Industrial and Operations Engineering at the University of Michigan, Ann Arbor from 1993-2003, the GM/NSERC Chair in Quality and Productivity at the University of Waterloo from 1988-1993, and before Waterloo, he taught in the Statistics Department at the University of Wisconsin from 1977-1988. He earned his BS in Mathematics from National Taiwan University in 1971 and PhD in Statistics from the University of California at Berkeley in 1976.
Prof Wu's honors include membership on the US National Academy of Engineering (2004), Member (Academician) of Academia Sinica (2000), COPSS (Committee of Presidents of Statistical Societies) Presidents Award in 1987, Honorary Professor at Chinese Academy of Sciences, and an Honorary Doctor of Mathematics at University of Waterloo. He is a Fellow of the American Society for Quality, of the Institute of Mathematical Statistics, and of the American Statistical Association. Prof Wu has won numerous awards, including Taiwan’s Penwenyuan Technology Award in 2008, the 1990 Wilcoxon Prize for the best paper in Technometrics, the 1992 Brumbaugh Award for the single most important paper to quality control among the publications sponsored by the American Society for Quality Control, and the Jack Youden Prize twice (1997, 2004) for best paper in Technometrics. He was the 1998 P. C. Mahalanobis Memorial Lecturer at the Indian Statistical Institutes with widely cited research work and a listing as an “ISI (Institute for Scientific Information) Highly Cited Researcher” in 2002.
Prof Wu’s work is widely cited in professional journals as well as in magazines, including a feature article about his work in Canadian Business and a special issue of Newsweek on quality. He has served as editor or associate editor for several prestigious statistical journals like Annals of Statistics, Journal of American Statistical Association, Technometrics, and Statistica Sinica. He has published more than 130 research articles in peer review journals.
Free and open to the public. Seating is on a first-come first-served basis.
