Faculty Directory

Xiao Liu

Xiao Liu

Assistant Professor

(ENGR)-Engineering

(INEG)-Industrial Engineering

Phone: 479-575-6033

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Research website: https://sites.google.com/site/liuxiaosite1/

Dr. Xiao Liu is an Assistant Professor at the Department of Industrial Engineering, University of Arkansas. Before that, he was a Research Staff Member (RSM) at IBM Thomas J. Watson Research Center, Yorktown Heights, New York (2015~2017), and IBM Smarter Cities Research Collaboratory Singapore (2012~2015). From 2013 to 2016, he served as an Adjunct Assistant Professor at the Department of Industrial and Systems Engineering, National University of Singapore. 

Dr. Liu's research is currently funded by the National Science Foundation and Industry.

Area 1: Physics-based Data-Driven Methodologies

  • PDE-based Spatio-Temporal Models for physical convection-diffusion processes
    • On-going investigations (applications):  
      • sensor-based environmental monitoring (e.g., urban pollution, wildfires, hurricane, ocean environment anomaly detection, and thermal map construction in data centers);
      • radar/satellite image modeling (e.g., cloud dynamics for short-term solar energy prediction, extreme weather events, etc.);
      • surrogate spatio-temporal models for stress/deformation on surface manifold based on Finite Element Analysis (e.g., air collision between aircraft and drones).
  • Engineering-Knowledge-Based Data-Driven Monitoring for complex engineered systems.
    • On-going investigations (applications):
      • Condition monitoring for reliability and energy efficiency of data center cooling systems.

Area 2: Tree-based Ensemble Statistical Learning for Recurrent Event Data

  • On-going investigations (applications):   
    • Intelligent food-borne outbreaks source investigation;
    • Reliability modeling and predictive maintenance for large-scale heterogeneous field systems.
  1. Applied/Engineering Statistics
  2. Statistical Learning
  3. Reliability and Statistical Process Control

At University of Arkansas (2017~ ): 

  1. Fall Semeter: INEG 2313: Applied Probability and Statistics (size: 80-100 undergraduate students)
  2. Spring Semeter: INEG 4163/5163: Statistical Learning and Applications for Industrial Problems (~ 40 undergraduate students; ~10 graduate students.

At National University of Singapore (2013~2016):

  1. Fall 2013, 2014, 2015 , IE5123 Reliability Engineering (Master of Science), Department of Industrial and Systems Engineering, National University of Singapore.
  2. Semester II, 2014-2015, IE5122 Statistical Quality Control (Master of Science), Department of Industrial and Systems Engineering, National University of Singapore.

At Qatar University (2011):

  1. Instructor, Semester II, 2011-2012, IENG320 Statistical Quality Control (undergraduate level), Department of Mechanical and Industrial Engineering, Qatar University.
  2. Instructor, Semester I, 2011-2012, IENG423 Design of Experiments (under graduate level), Department of Mechanical and Industrial Engineering, Qatar University.

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

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

Journal papers: [*student author]
  1. *Hajiha, M., Liu, X., and Hong, Y. (2020+), “Degradation Modeling under Dynamic Operating Conditions and Its Applications”, Journal of Quality Technology, accepted. data: https://github.com/dnncode/LTPP-Data
  2. Liu, X., and Pan, R., (2020), “Analysis of Large Heterogeneous Repairable System Reliability Data with Static System Attributes and Dynamic Sensor Measurement in Big Data Environment”, Technometrics, 62, 206-222. open-source code: https://github.com/dnncode/Random-Forest-for-Recurrence-Data
  3. Liu, X. (2020+), "A Simple Procedure for Analyzing Reliability Data from Double-Stage Accelerated Life Tests", Quality Technology & Quantitative Management, accepted. open-source codehttps://github.com/dnncode/PDA
  4. Yeo, K.M., Hwang, Y.D., Liu, X., and Kalagnanam (2019), “Development of a spectral source inverse model by using generalized polynomial chaos”, Computer Methods in Applied Mechanics and Engineering, 347, 1-20. *Impact Factor: 4.441; 2/103 under Google scholar—Mathematics and Interdisciplinary Applications
  5. Bowen. S, Hippe, D, Chaovalitwongse, W., Duan, C., Thammasorn, P., Liu, X., Miyaoka, R., Vesselle, H., Kinahan, P., Rengan, R., and Zeng, J., (2019), "Forecast for Precision Oncology: predicting spatially variant and multiscale cancer therapy response on longitudinal quantitative molecular imaging," Clinical Cancer Research, accepted. *Impact factor: 10.199.
  6. Liu, X., Yeo, K.M., and Kalagnanam, J., (2018), “A Statistical Modeling Approach for Spatio-Temporal Degradation Data”, Journal of Quality Technology, 50(2), 166--182. *Special issue on reliability and maintenance modeling with big data.
  7. Liu, X., Gopal, V. and Kalagnanam, J., (2018), “A Spatio-Temporal Modeling Framework for Weather Radar Image Data in Tropical Southeast Asia”, Annals of Applied Statistics, 12(1), 378-407. open-source code: https://github.com/dnncode/STCAR_Radar_Image
  8. 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”, Annals of Applied Statistics, 10, 756-785.
  9. Liu, X. and Tang, L.C. (2016), “Reliability and Spares Provisioning for Line Replaceable Units with Time-Varying Fleet Size”, IIE Transactions, 48, 43-56. Featured in Industrial Engineer Magazine, Dec 2016
  10. 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.
  11. 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.
  12. 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.
  13. Liu, X. and Tang, L.C. (2013), “Planning Accelerated Life Tests with Scheduled Inspections for Log-Location-Scale Distributions”, IEEE Transactions on Reliability, 62, 515-526.
  14. Liu, X. (2012), “Planning of Accelerated Life Tests with Dependent Failure Modes Based on a Gamma Frailty Model”, Technometrics, 54, 398-409.
  15. 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. Featured in Industrial Engineer Magazine, Mar 2013
  16. Liu, X. and Tang, L.C. (2012), “Analysis for Reliability Experiments under Subsampling”, Quality Technology and Quantitative Management, 10, 141-160. Special issue: Reliability Modeling, Inference and Analysis,
  17. Liu, X. and Qiu, W.S. (2011), “Modelling and Planning of Step-Stress Accelerated Life Tests with Multiple Causes of Failure”, IEEE Transactions on Reliability, 60(4), 712-720.
  18. Liu, X. and Tang, L.C. (2010), “Accelerated Life Test Plans for Repairable Systems with Independent Competing Risks”, IEEE Transactions on Reliability, 59(1), 115-127.
  19. Tang, L.C. and Liu, X. (2010), “Planning and Inference for a Sequential Accelerated Life Test”, Journal of Quality Technology, 42(1), 103-118.
  20. Liu, X. and Tang, L.C. (2010), “A Bayesian Planning Method 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.
  21. Liu, X. and Tang, L.C. (2010), “Statistical Planning of Sequential Constant-Stress Accelerated Life Test with Stepwise Loaded Auxiliary Acceleration Factor”, Journal of Statistical Planning and Inference, 140, 1968-1985.
  22. 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.
Patent:
  1. “Airborne particulate source detection system”. US Patent US20160377430A1.
  2. “Detection Algorithms for Distributed Emission Sources of Abnormal Events”. US Patent US20170147927A1.

Professional Membership

  • American Statistical Association (ASA)
  • Institute of Industrial and Systems Engineer (IISE)
  • Institute for Operations Research and the Management Sciences (INFORMS) 
  1. 2019 NSF EPSCoR Research Fellow.
  2. 2018 Statistics in Physical Engineering Sciences (SPES) Award, American Statistical Association (ASA). (http://www.amstat.org/ASA/Your-Career/Awards/Statistics-in-Physical-Engineering-Sciences-Award.aspx)
  3. 2017 Best Paper Award, Prognostics and System Health Management Conference (PHM-Harbin).
  4. 2017 IBM Outstanding Technical Achievement Award for health monitoring and prediction for large-scale Engineering, Procurement and Construction (EPC) projects with FLUOR.
  5. 2016 INFORMS Quality, Statistics and Reliability Section (QSR) Best Refereed Paper Award
  6. 2015 IBM Outstanding Technical Achievement Award for the Predictive Environmental Analytics System (PEAS) with the National Environmental Agency Singapore, 2012-2015.
  7. 2015 First Patent Application Invention Achievement Award for: Airborne Particulate Source Detection System.
  8. 2015 Global Business Services (GBS) Service Excellence Award (SEA) for the IBM-Micron pilot project.
  9. 2014 IBM Manager's Choice Award for the NEA project.
  10. 2011 Ralph A. Evans/P.K. McElroy Award for Best Paper (shared with my Ph. d advisor, Dr. Loon-Ching Tang) at the Annual Reliability and Maintainability Symposium 2012, Reno, Nevada, USA. 
  11. 2010 National Semiconductor Gold Medal, Department of Industrial and Systems Engineering, National University of Singapore (http://www.ise.nus.edu.sg/msc_students/honourroll-year.html)