【Time】June 19th, 2013 (Wednesday)  10:00~11:00AM       

【Venue】Room N510, Shunde Building

【Speaker】Dr. Chia-Jung Chang, Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University

【Host】Dr. Kaibo Wang

【Title】Engineering Driven Data Fusion for System Modeling and Performance Improvement

【Abstract】The rapid advancements in sensor technology, communication networks, and computing power have resulted in temporally and spatially dense data-rich environments in a variety of manufacturing, service, health care and energy systems. How to effectively develop system level modeling and analysis in this data-rich environment raises research challenges due to inherent system complexity, data heterogeneity, and uncertainty propagation, but also provides the unprecedented opportunities for improving complex system performance.

In today’s presentation, I will provide an overview of ongoing engineering driven data fusion research which emphasizes on integrating advanced statistical methodologies with domain knowledge to develop a set of new data analysis methods to fully utilize online sensing for manufacturing process modeling and control. Examples of methodological developments and their applications will be discussed in order to demonstrate the characteristics of system informatics research and to emphasize the need for multidisciplinary system integration efforts.

Detailed discussion will focus on the methodological development of model enhancement which mitigates the individual drawbacks of physics-based models and statistics models to provide models with better accuracy by combining the strengths of the two models. Afterwards, a brief overview of my other research topics will also be presented. Please contact Chia-Jung Chang at: cchang@psu.edu.

【Biography】Dr. Chia-Jung Chang is currently an Assistant Professor in Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at Pennsylvania State University.  She received Ph.D. degree in Industrial Engineering and M.S. degree in Statistics at Georgia Institute of Technology, and her B.S. and M.S. degrees in Industrial Engineering at National Tsing-Hua University in Taiwan.  Dr. Chang’s research interests focus on the fusion of advanced statistics, domain knowledge, and control theory to develop methodologies for modeling, monitoring, diagnosis, and improvement for complex systems in data rich environments.  She serves as the Associate Editor of Asian Journal of Industrial and Systems Engineering since 2012.  She is a member of the Institute of Industrial Engineering (IIE), Institute of Operations Research and Management (INFORMS), American Statistical Association (ASA), and American Society of Mechanical Engineers (ASME).