Smart Calibration Through Deep Learning for High-Confidence and Interoperable Cyber-Physical Additive Manufacturing Systems

Qiang Huang received his Ph.D. degree in Industrial and Operations Engineering from the University of Michigan-Ann Arbor. He is currently an Associate Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research interests include Integrated Nanomanufacturing & Nanoinformatics; Foundations of Quality Control for Additive Manufacturing, and Effect Equivalence Methodology for Modeling, Inference, Transfer Learning, and Control. He was the holder of Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received National Science Foundation CAREER award in 2011 and IEEE Transactions on Automation Science and Engineering Best Paper Award from IEEE Robotics and Automation Society in 2014. He is an Associate Editor of IEEE Transactions on Automation Science and a Guest Editor for Journal of Quality Technology. He was Associate Editor for IEEE Robotics and Automation Letters from 2015 to 2016, and a member of the scientific committee (Editorial Board) for the North American Manufacturing Research Institution (NAMRI) of SME, 2009-2011 and 2013-2015. He is a member of IEEE, INFORMS, ASME and IIE.

Arman Sabbaghi is an Assistant Professor in the Department of Statistics at Purdue University. His research interests include model building for improved quality control of complex engineering systems, Bayesian data analysis, and experimental design. He received his Ph.D. degree in Statistics from Harvard University in May 2014.