HKU IDS Distinguished Speaker Series:
Data Modality, Data Science, and Multistage Manufacturing
Abstract
Modern manufacturing processes invariably involve multiple machines, stations, or operations to produce a final product, the quality of which is a result of the complex interactions on these stages. The quality characteristics at one stage are not only influenced by local variations but also by variations propagated from operations at upstream stages. Advancement in sensing technologies and data science methodologies unleashed a paradigm shift in research and development of modeling, analysis, and control of multistage manufacturing. This presentation shares the speaker’s perspective on how the co-evolution of sensing data modality and data science propels the advancement in variation modeling and analysis of multistage manufacturing. The speaker will also discuss in detail the challenges and opportunities in applying AI and machine learning to address practical needs in multistage manufacturing and enable smart manufacturing. Examples of past and ongoing research projects will be used to illustrate and exemplify the frontiers of this research area. All examples involve the development of fundamental methodologies motivated by industrial demands and their implementation in real-life multistage manufacturing systems.
Speaker
Prof Jianjun SHI
Carolyn J. Stewart Chair and Professor
Georgia Institute of Technology
Member, U.S. National Academy of Engineering
Prof Jianjun SHI is the Carolyn J. Stewart Chair and Professor in H. Milton Stewart School of Industrial and Systems Engineering, with joint appointment in the George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. His research interests focus on data fusion for quality improvements, with emphasis on integration of system informatics, advanced statistics and machine learning, and control theory for the design and operational improvements in advanced manufacturing applications. The technologies developed in Prof SHI’s research group have been widely implemented in various production systems with significant economic impacts.
Prof SHI is a member of the National Academy of Engineering (NAE) of USA, an Academician of the International Academy for Quality, and a Fellow of ASME, IISE, INFORMs, ISI, and SME. He received numerous awards and recognitions, including the ASA Deming Lecturer Award, the ASQ Shainin Metal, the ENBIS George Box Medal, the ASQ Walter Shewhart Medal, the SME/NAMRI S. M. Wu Research Implementation Award, the ASQ Brumbaugh Award, IISE David F. Baker Distinguished Research Award, and the IIE Albert G. Holzman Distinguished Educator Award. He is the founding chair of the Quality, Statistics and Reliability (QSR) Division at INFORMS. He served as the Editor-in-Chief of the IISE Transactions (2017-2020), the flagship journal of the Institute of Industrial and Systems Engineers.
More information about Prof Shi can be found at https://sites.gatech.edu/jianjun-shi/
Moderator
Prof Qingpeng ZHANG
Associate Professor @ HKU IDS & HKUMed Prof Qingpeng ZHANG is an Associate Professor at the Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University of Hong Kong. His research lies at the intersection of data science and medicine, with a focus on developing interpretable machine learning and evolutionary modeling to understand complex biological systems. His work has appeared in journals such as Nature Human Behaviour, Nature Communications, and PNAS, and has been highlighted in media outlets such as The Washington Post, The New York Times, The Times, CNN, and BBC. For full biography of Prof. Zhang, please refer to: https://datascience.hku.hk/people/qingpeng-zhang/
