Haitao Liao, professor of industrial engineering, was recently the recipient of a National Science Foundation Award. The award will fund developing new methodologies for automated knowledge discovery from complex reliability and healthcare data with covariates.

Collecting and analyzing data with covariates (e.g., temperature, humidity, and radiation level) are critical activities in science and engineering. An important example is accelerated life testing data used in the design of products, such as lithium-ion batteries. Such data are collected by exposing test units to harsher-than-normal conditions to expedite the failure process.

The resulting failure times are modeled by a probability distribution and a life-stress relationship. However, if the probability distribution and/or the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This is extremely important when researchers are expecting to develop highly reliable nanomaterials and devices.

Other examples are widely seen in healthcare systems, where it is crucial to quantify probability distributions of patients' length-of-stay, waiting time, and disease progression, and the effects of influential covariates on these measures.

By collaborating with researchers in the University of Arizona and Biomedical Informatics Services at the same institution, Liao will investigate new methodologies to overcome the research challenges using matrix-analytic models. Statistical tools and optimization algorithms will be developed for efficiently collecting such data or selecting the useful subsets from massive data for quick implementations.

The research findings will help the team create a new avenue for modeling and interpreting such data in situations where the data-generating mechanisms are unknown or difficult to analyze using existing statistical tools. In addition to advancing reliability theory, the project will have potential impacts on manufacturing, healthcare, energy, transportation, and aerospace industries.

Ed Pohl head of the Department of Industrial Engineering stated "We are very proud of Dr. Liao's award.  His research complements nicely our growing interest in the area of big data and analytics.  His work blends nicely the use of statistical tools and operations research techniques and will have broad application in industry."

Haitao Liao holds the James M. Hefley and Marie G. Hefley Professor of Logistics and Entrepreneurship in the College of Engineering. He received his master's and doctorate in industrial and systems engineering, as well as a master's degree in statistics from Rutgers University. He earned his bachelor's degree in electrical engineering at the Beijing Institute of Technology.

Liao has held faculty positions at Wichita State University, the University of Tennessee and the University of Arizona. He also served as a postdoctoral researcher at the National Science Foundation Center for Intelligent Maintenance Systems.

Currently Liao serves as associate editor of the Journal of Quality Technology, Quality Technology and Quantitative Management, and the Journal of Industrial and Production Engineering.

He is the recipient of the National Science Foundation CAREER Award in 2010, the winner of the IIE 2010 & 2013 William A.J. Golomski Award, and the winner of the 2015 Stan Ofsthun Award. He is the immediate past Chair of INFORMS Quality, Statistics and Reliability (QSR) Section, and the President of IIE Quality Control and Reliability Engineering (QCRE) Division.