Robust Engineering Design (RED) has drawn in many areas of statistics, but maybe especially experimental design and stochastic processes. The work has been highly successful, particularly when fused with computer experiments (surrogate or meta-modelling). But there remains some separation: statisticians are perhaps too content to treat engineering models as black boxes and engineers resist making use of the more advanced statistical methods. But under the umbrella of Uncertainty Quantification (UQ), these barriers are finally breaking down. The area is sketched briefly and examples given of new interface areas taken from the speaker's own collaborations.About the Speaker
Henry P. Wynn (BA University of Oxford, PhD Imperial College, London 1970) is Emeritus Professor of Statistics at the London School of Economics (LSE). He was head of the LSE Department of Statistics in 2003-2006, and part-time Scientific co-Director of EURANDOM (Netherlands) in 2000-2005. Before that he was Professor of Mathematical Statistics, Dean of Mathematics and co-founder of the Engineering Design Centre at City University, London, and then Professor of Industrial Statistics and founding Director of the Risk Initiative and Statistical Consultancy Unit at the University of Warwick. He has published widely in theoretical and applied statistics, with an emphasis on engineering applications. He holds the Guy Medal in Silver from the Royal Statistical Society, the Box Medal from the European Network for Business and Industrial Statistics, is an Honorary Fellow of the UK Institute of Actuaries and a Fellow of the Institute of Mathematical Statistics.
In advanced manufacturing systems, the rapid advances in cyber-infrastructure ranging from sensor technology and communication networks to high-powered computing have resulted in temporally and spatially dense data-rich environments. With massive data readily available, there is a pressing need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the rich data streams communicated by the contemporary complex engineering systems, (ii) the extraction of pertinent knowledge about the environmental and operational dynamics driving these systems, and (iii) the exploitation of the acquired knowledge for more enhanced design, analysis, diagnosis, and control of them.
Addressing this need is considered very challenging because of a collection of factors, which include the inherent complexity of the physical system itself and its associated hardware, the uncertainty associated with the system’s operation and its environment, the heterogeneity and the high dimensionality of the data communicated by the system, and the increasing expectations and requirements posed by real-time decision-making. It is also recognized that these significant research challenges, combined with the extensive breadth of the target application domains, will require multidisciplinary research and educational efforts.
This presentation will discuss some research challenges, advancements, and opportunities in synergies of engineering and statistics for system performance improvement. Specific examples will be provided on research activities related to the integration of statistics and engineering knowledge in various applications. Real case studies will be provided to illustrate the key steps of system research and problem solving, including (1) the identification of the real need and potential in problem formulation; (2) acquisition of a system perspective of the research; (3) development of new methodologies through interdisciplinary methods; and (4) implementation in practice for significant economical and social impacts. The presentation will emphasize the examples of research achievements, as well as how the achievements were achieved.About the Speaker
Dr. Jianjun Shi is the Carolyn J. Stewart Chair and Professor at H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. Prior to joining Georgia Tech in 2008, he was the Lawton and Johnson Chair Professor of Engineering, Professor of the Department of Industrial and Operations Engineering, and Professor of the Department of Mechanical Engineering at the University of Michigan. He received his B.S. and M.S. in Electrical Engineering at the Beijing Institute of Technology in 1984 and 1987, and his Ph.D. in Mechanical Engineering at the University of Michigan in 1992.
Professor Shi's research interests focus on system informatics and control for the design and operational improvements of manufacturing and service systems. He has published one book and more than 160 papers (110+ Journal papers, and collectively received about 5500+ paper citations). Professor Shi is the founding chairperson of the Quality, Statistics and Reliability (QSR) Subdivision at INFORMS. He is currently serving as the Focus Issue Editor of IIE Transactions on Quality and Reliability Engineering; Associate Editor of ASME Transactions, Journal of Manufacturing Science and Engineering; Editor of Journal of Systems Science and Complexity; and Senior Editor of Chinese Journal of Institute of Industrial Engineering. He is a Fellow of IIE, a Fellow of ASME, a Fellow of INFORMS, an Elected Member of the International Statistical Institute (ISI), an Academician of the International Academy for Quality, and a life member of the American Statistics Association (ASA).
Xiao-Li Meng, Dean of the Harvard University Graduate School of Arts and Sciences (GSAS), Whipple V. N. Jones Professor and former chair of Statistics at Harvard, is well known for his depth and breadth in research, his innovation and passion in pedagogy, and his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng has received numerous awards and honors for the more than 150 publications he has authored in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development; he has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a regularly appearing column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, frequentist, and fiducial perspectives; quantify ignorance via invariance principles; multi-phase and multi-resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard as Professor of Statistics, where he was appointed department chair in 2004 and the Whipple V. N. Jones Professor in 2007. He was appointed GSAS Dean on August 15, 2012.