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About Haitao Liao

Haitao Liao is an Associate Professor in the Systems and Industrial Engineering Department at the University of Arizona, Tucson. Dr. Liao received his Ph.D. in Industrial and Systems Engineering from Rutgers University. He holds a bachelors degree in Electrical Engineering from Beijing Institute of Technology, Beijing, China and masters degrees in Statistics and Industrial Engineering both from Rutgers University.
At the University of Arizona, he teaches courses in Reliability Engineering, Maintainability Engineering, and Engineering Statistics. He is currently the Director of the Reliability and Intelligent Systems Engineering (RISE) Laboratory. The laboratory research is directed toward the development of diagnostic and prognostic methods for complex engineering systems, the modeling and analysis of reliability testing and service logistics, and the development of instrument and control technologies.
Dr. Liao’s current research efforts focus on accelerated testing, coordination of life cycle reliability and service logistics, applied statistics, renewable energy, and energy saving technologies. His research is funded by the National Science Foundation, the Department of Energy, the Nuclear Regulatory Commission, Hong Kong Research Grant Council, and Industry. He is an Assistant Area Editor of Computers & Industrial Engineering, and is the author of more than fifty peer reviewed publications. He is a recipient of the National Science Foundation CAREER Award in 2010.
He is the father of a little girl, Jessica, who has brought joys and special meanings to his family since 2008.


Present Faculty Member, University of Arizona

Research Interests

Industrial Engineering, Reliability Engineering, and Applied Statistics

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Honors and Awards

  • Quest Scholar of the Week, the week of 02/12/2010, University of Tennessee.
  • NSF CAREER Award, 02/2010.
  • The Best Student Paper Competition Finalist, Quality Statistics and Reliability (QSR) Section, INFORMS, Atlanta, GA, 10/2003. Title: “Customer-Oriented Reliability Verification and Robust Design Optimization for Highly Reliable Prod- ucts under Stochastic Use Conditions”.

Articles (12)