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Screen Shot 2016-08-29 at 11.33.37 AMJoin host Feng Ju for the IE Decision Systems Engineering Fall ’16 Seminar Series for a talk on the rapid advancement of distributed sensing and imaging technology in healthcare and manufacturing and how to address the related predictive modeling challenges through a novel physics-driven spatiotemporal regularization method.

Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling
Speaker: Hui Yang, The Harold and Inge Marcus Dept. Of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park
IE Decision System Engineering Fall ’16 Seminar Series
September 9, 2016
Brickyard Engineering (BYENG) 210, Tempe campus [map]


Rapid advancement of distributed sensing and imaging technology brings the proliferation of high-dimensional spatiotemporal data, i.e., y(s; t) and x(s; t) in manufacturing and healthcare systems. Traditional regression is not generally applicable for predictive modeling in these complex structured systems. For example, infrared cameras are commonly used to capture dynamic thermal images of 3D parts in additive manufacturing. The temperature distribution within parts enables engineers to investigate how process conditions impact the strength, residual stress and microstructures of fabricated products. The ECG sensor network is placed on the body surface to acquire the distribution of electric potentials y(s; t), also named body surface potential mapping (BSPM). Medical scientists call for the estimation of electric potentials x(s; t) on the heart surface from BSPM y(s; t) so as to investigate cardiac pathological activities (e.g., tissue damages in the heart). However, spatiotemporally varying data and complex geometries (e.g., human heart or mechanical parts) defy traditional regression modeling and regularization methods. This talk will present a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex manufacturing and healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. In the end, we will introduce our lab at Penn State and future research directions will also be discussed.

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