Title：Data Decomposition for Advanced Analytics of Complex Engineering Systems
Time：10:00 to 11:00, Jul.29, 2019
Place：F207, School of Mechanical Engineering
Host：XIA Tangbin, Associate Professor (Department of Industrial Engineering and Management)

Biography
Xiaowei Yue is an assistant professor at the Grado Department of Industrial and Systems Engineering, Virginia Tech. He got his Ph.D. in industrial engineering, M.S. in Statistics from Georgia Tech, M.S. in Engineering Thermo-physics from Tsinghua, B.S. in Mechanical Engineering from Beijing Institute of Technology. His research interests focus on engineering-driven data analytics for advanced manufacturing. The objective is to develop new methodologies for predictive modeling, uncertainty quantification, system optimization, and model based engineering (MBE). He won Mary G. and Joseph Natrella Scholarship from American Statistical Association, and IISE Pritsker Doctoral Dissertation Award, Early Career Travel Awards from ASA and ASQ, and several best paper awards, e.g. IEEE Transactions on Automation Science and Engineering Best Paper Award, etc.

Abstract
Data decomposition is an important step for high-dimensional data analytics of complex engineering systems, but it is less emphasized in our current data analytics domain. This paper summarizes the key techniques for data decomposition, and separates them into two categories. One is deterministic decomposition, and the other is stochastic decomposition. The deterministic decomposition captures geometric or algebraic shape from the high-dimensional datasets directly, which is efficient for feature extraction and dimensionality reduction; while the stochastic decomposition provides probabilistic descriptions, and corresponding statistical distributions are estimated from the datasets. A novel methodology framework of data decomposition is proposed to formulate the existing approaches. These methods have been applied into several advanced manufacturing scenarios. Based on this methodology framework, some future research opportunities for new methodology development are discussed for data analytics of engineering systems.