Faculty Directory

Xiao Liu

Xiao Liu

Assistant Professor

College of Engineering

(INEG)-Industrial Engineering

Phone: 479-575-6033

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Personal webpage: https://sites.google.com/site/liuxiaosite1/

I am an Assistant Professor at the Department of Industrial Engineering, University of Arkansas (June 2017~present). Before that, I was a Research Scientist at IBM Thomas J. Watson Research Center, Yorktown Heights, New York (2015~2017), and IBM Research Collaboratory Singapore (2012~2015). I also served as an Adjunct Assistant Professor at the Department of Industrial and Systems Engineering, National University of Singapore (2013~2016), a postdoctoral researcher on a joint project between Rutgers University, New Jersey, and Qatar University, Doha, Qatar (2011~2012), and I am currently on the editorial board of Quality and Reliability Engineering International (Apr 2016~Present). 

My research focuses on engineering probability and statistics, spatio-temporal modeling, big data analytics, and various engineering-knowledge-based data-driven methodologies in broad areas such as quality and reliability, manufacturing yield prediction, preventive maintenance, urban air quality modeling, extreme weather events prediction, etc.  

My work has been published in peer-reviewed journals including Technometrics, Annals of Applied Statistics, IEEE Transactions on Reliability, Journal of Quality Technology, IIE Transactions, etc. I received the prestigious Ralph A. Evans/P.K. McElroy Award for the best paper at RAMS 2011, Best Refereed Paper Award of the Quality, Statistics and Reliability (QSR) section at INFORMS 2016, IBM Outstanding Technical Achievement Award in both 2015 and 2017, and 2018 Statistics in Physical and Engineering Sciences (SPES) Award from the American Statistical Association (ASA). 

(PhD and Research Assistant Positions are available; please contact me if you are interested)

Engineering-knowledge-based data-driven methodologies in broad areas including

  • Statistical spatio-temporal modeling for data arising from physical convection-diffusion processes (e.g., air quality, extreme weather events, etc.)
  • Statistical methods for reliability engineering (e.g., optimum design of reliability testing experiments, stochastic degradation processes, optimum predictive maintenance, etc.)
  • Big Data analytics for engineering applications (e.g., additive tree-based methods for recurrence data with dynamic covariates, manufacturing yield prediction for semiconductor wafer, etc.)

INEG 2313: Applied Probability and Statistics

INEG 410V/514V: Special Topics in Industrial Engineering (Modern Statistical Techniques for Real Industrial Problems)

Ph. D, Industrial and Systems Engineering, National University of Singapore

B.Eng, Mechanical Engineering, Harbin Institute of Technology, China 

Journal papers:

  1. Yeo, K.M., Hwang, Y.D., Liu, X., and Kalagnanam, J. (2019), "Development of hp-inverse model by using generalized polynomial chaos", Computer Methods in Applied Mechanics and Engineering, 347, 1-20.
  2. Liu, X., Yeo, K.M. and Kalagnanam, J. (2018),  "A Statistical Modeling Approach for Spatio-Temporal Degradation Data", Journal of Quality Technology50, 166--182. Special issue on "reliability and maintenance modeling with big data".
  3. Liu, X., Gopal, V., and Kalagnanam, J. (2018), "A Spatio-Temporal Modeling Framework for Weather Radar Image Data in Tropical Southeast Asia",  The Annals of Applied Statistics, 12(1), 378-407.
  4. Liu, X., Yeo, K.M., Hwang, Y.D., Singh, J. and Kalagnanam, J. (2016) "A Statistical Modeling Approach for Air Quality Data Based on Physical Dispersion Processes and Its Application to Ozone Modeling", The Annals of Applied Statistics, 10(2), 756-785.
  5. Liu, X. and Tang, L.C. (2016) "Reliability Analysis and Spares Provisioning for Repairable Systems with Dependent Failure Processes and Time-Varying Installed Base", IIE Transactions, 48, 43--56. (Featured in Industrial Engineer Magazine Dec 2015). 
  6. Yeo, K., Hwang, Y., Liu, X. and Kalagnanam, J. (2016), "Stochastic Optimization Algorithm for Inverse Modeling of Air Pollution", Bulletin of the American Physical Society, 61.
  7. Singh, J., Yeo, K., Liu, X., Hosseini, R., and Kalagnanam, J. (2016), "Evaluation of WRF model seasonal forecasts for tropical region of Singapore", Advanced in Science and Research, 12, 69-72.
  8. Liu, X., Al-Khalifa. K., Elsayed, A.E., Coit, D.W, and Hamouda, A.M. (2014) "Criticality Measures for Components with Multi-Dimensional Degradation", IIE Transactions, 46, 987–998.
  9. Liu, X. and Tang, L.C. (2013) "Planning Accelerated Life Tests with Scheduled Inspections for Log-Location-Scale Distributions", IEEE Transactions on Reliability, 62(2), 515 - 526.
  10. Liu, X. (2012) "Planning of Accelerated Life Tests with Dependent Failure Modes Based on a Gamma Frailty Model", Technometrics, 54(4), 398-409. AMSTATNEWS:  http://magazine.amstat.org/blog/2012/11/01/design-and-model-selection/ 
  11. Liu, X., Li, J.R., Al-Khalifa. K., Hamouda, A.M., Coit, D.W, and Elsayed, A.E., (2012) "Condition-Based Maintenance for Continuously Monitored Degrading Systems with Multiple Failure Modes", IIE Transactions, 45, 422-435, (Among the top 10 most read article in IIE Transactions as of Sep 2015; Featured in Industrial Engineer Magazine, Mar 2013)
  12. Liu, X. and Tang, L.C. (2012) "Analysis for Reliability Experiments under Blocking", Journal of Quality Technology and Quantitative Management, Special Issue: Reliability Modeling, Inference and Analysis, 10, 141-160.
  13. Liu, X. and Qiu, W.S. (2011) "Modelling and Planning of Step-Stress Accelerated Life Tests with Independent Competing Risks", IEEE Transactions on Reliability, 60(4), 712-720. Top accessed article of the journal in Jan 2012 (rank:2)
  14. Liu, X. and Tang, L.C. (2010) "Accelerated Life Test Plans for Repairable Systems with Multiple Independent Risk", IEEE Transactions on Reliability, 59(1), 115-127. (2010 National Semiconductor Gold Medal, ISE Department, National University of Singapore).
  15. Tang, L.C. and Liu, X. (2010) "Planning and Inference for a Sequential Accelerated Life Test", Journal of Quality Technology, 42(1), 103-118.
  16. Liu, X. and Tang, L.C. (2010) "A Bayesian Optimal Design for Accelerated Degradation Tests", Quality and Reliability Engineering International, 26(8), 863-875. Special Issue: Business and Industrial Statistics: Developments and Industrial Practices in Quality and Reliability.
  17. Liu, X. and Tang, L.C. (2010) "Planning Sequential Constant-Stress Accelerated Life Tests with    Stepwise Loaded Auxiliary Acceleration Factor", Journal of Statistical Planning and Inference, 140(7), 1968-1985.
  18. Liu, X. and Tang, L.C. (2009) "A Sequential Constant-Stress Accelerated Life Testing Scheme and Its Bayesian Inference", Quality and Reliability Engineering International, 25(1), 91-109.

US Patent:

  1. Kalagnanam, J. Liu, X., Yeo, K.M., and Zhou, Y.S., “Airborne particulate source detection system”. US Patent No.: 9995849, 2018/6/12.

  2. Hwang, Y.D., Kalagnanam, J., Liu, X., and Yeo, K.M. “Detection algorithms for distributed emission sources of abnormal events”. US Patent Appl. No.: 20170147927A1 , 2017/05/25.

Professional Services:

  • Editorial Board, Quality and Reliability Engineering International, 2016~present.
  • Council Member, QSR (Quality, Statistics and Reliability) Section, INFORMS, 2018-2020.
  • Organization Committee (special/invited sessions), 2019 INFORMS Conference on Service Science, June 27-29, Nanjing, China.
  • Program Committee, 2019 AAAI Conference on Artificial Intelligence, Jan 27-Feb 1, Honolulu, Hawaii.
  • Program Committee, The 3nd IEEE International Workshop on Big Spatial Data in conjunction with 2018 IEEE International Conference on Big Data (IEEE BigData 2018), Dec 10-13, 2018, Seattle, WA.
  • Conference sub-committee, INFORMS 2018, Quality, Statistics and Reliability (QSR) section .
  • Program Committee, The 12th International Conference on Reliability, Maintainability and Safety, Oct 17-19, 2018, Shanghai, China.
  • Program Committee, The 2nd IEEE International Workshop on Big Spatial Data in conjunction with 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Dec 11-14, 2017, Boston, MA.

Professional Membership

  • American Statistical Association (ASA)
  • Institute of Industrial and Systems Engineer (IISE)
  • Institute for Operations Research and the Management Sciences (INFORMS)