George Runger

George Runger is a Professor at the School of Computing and Augmented Intelligence, Arizona State University. His work focuses on analytical methods for knowledge generation and data-driven organizational improvements, particularly in machine learning for large-scale data, real-time analysis, and applications to surveillance, decision support, and population health. Previously, he was a senior engineer and technical leader at IBM.

Education:

• Ph.D. in Statistics, University of Minnesota (1982)

Runger's research bridges machine learning, data mining, and statistical process control (SPC) to address challenges in manufacturing, healthcare, and semiconductor systems. His work includes developing artificial contrasts for signal detection, ensemble feature selection, and self-learning decision rules for adaptive SPC.

His funded projects span NSF, DOD-NAVY-ONR, and Semiconductor Research Corporation grants, emphasizing supply chain analysis, dimensional metrology, and energy efficiency diagnostics. He has co-authored foundational texts like Applied Statistics and Probability for Engineers and Engineering Statistics.

Scientific Awards:

• Inaugural Department Editor for Healthcare Informatics, INFORMS Transactions on Healthcare Systems Engineering

Runger actively contributes to academia as a reviewer for journals like Management Science and IEEE Transactions on Knowledge and Data Engineering, and as a panel member for NSF and INFORMS workshops. He co-directs ASU's Quality and Reliability Engineering Laboratory and the Modeling and Analysis of Semiconductor Manufacturing team.