Qing (Cindy) Chang
About
Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia. Her research focuses on math-based and data-driven modeling and analysis of dynamic manufacturing systems, enhancing manufacturing efficiency and sustainability through adaptive control and machine learning, and advancing human-robot collaboration in industrial settings.
Before joining UVA in 2019, she was an associate professor of mechanical engineering at Stony Brook University. She received an NSF CAREER Award and was recognized by SME as one of the “20 Most Influential Professors in Smart Manufacturing.” Prior to her academic career, she spent a decade at the General Motors Global Research & Development Center, where she received the company’s highest corporate innovation honor, the Boss Kettering Award, three times for her contributions to improving production efficiency and quality.
She is an elected Fellow of ASME and SME (the Society of Manufacturing Engineers). She has served on the board of directors of NAMRI/SME (the North American Manufacturing Research Institution of SME) and has held leadership and editorial roles across multiple societies, journals, and conferences affiliated with ASME, IEEE, and SME.
Education
M.S. University of Wisconsin - Madison
Ph.D. in Manufacturing, University of Michigan – Ann Arbor
Research Interests
Awards
-
Q&A: UVA’s Cindy Chang Talks About Her AI Tool for Optimizing Manufacturing Processes
Read NowHer lab’s approach finds the connections between individual processes and the manufacturing system as a whole — and tries to coordinate everything at once, she tells Tech Briefs.
-
UVA Researchers Pioneer AI-Driven Manufacturing Efficiency
Read NowCindy Chang and her team have created a more efficient way to optimize manufacturing systems, improving speed and quality while reducing waste.
-
Need To Know: Engineering Skills To Get a Job in the ‘New Industrial Revolution’
Read NowProfessor Qing (Cindy) Chang explains how students can prepare to find work in the modern manufacturing industry, including the automotive sector.