The Quality and Data Analytics Lab at HKUST (established in 1997 by Prof. Fugee Tsung) engineers quality intelligence solutions for real-world industrial challenges by integrating Six Sigma, statistical learning, and machine learning. We design bespoke quality strategies and actionable implementation roadmaps—tailored to diverse products and services—that drive continuous improvement and deliver measurable business impact. Building on decades of scholarly research, training, and consulting in manufacturing and service quality engineering and industrial Big Data, the Lab leverages Six Sigma problem-solving methodologies and cutting-edge academic insights to advance quality analytics and operational excellence across Greater China and globally.

Prof. Tsung is a globally recognized expert in industrial analytics and quality engineering, listed among the top 2% of most influential scientists worldwide by Stanford-Elsevier. As a Chair Professor at HKUST, he directs the Industrial Informatics and Intelligence Institute (Triple-I Institute) and the Quality and Data Analytics Lab (QLab). He has held prominent positions such as Editor-in-Chief for the Journal of Quality Technology (JQT), Head of the Department of Industrial Engineering and Decision Analytics, and founding acting Dean of the Information Hub at HKUST(GZ). A Fellow of esteemed organizations like ASA, ASQ, IISE, IAQ, and HKIE, he was awarded the 2025 ASQ Shewhart Medal, the highest honor in quality-related theory, methods, and practice, and the winner of the 2025 IISE George L. Smith International Awards for Excellence in Promotion of Industrial Engineering Award.

Industrial Informatics and Intelligence Institute, also known as the Triple-I Institute, is committed to achieving the following objectives:

• Establishing a new scientific foundation for the design, analysis, and control of complex manufacturing and service systems.
• Bridging the gap between data analytics, engineering systems, and industrial practice by integrating systems and information with data-driven, model-driven, and problem-driven methodologies.