Xiao Liu

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 focuses on Physics-Informed and Domain-Aware Data-Driven Methodologies for Engineering Applications.

Education & Degrees

• BS, Mechanical Engineering, Harbin Institute of Technology, Harbin
• PhD, Industrial and Systems Engineering, National University of Singapore, Singapore

Companies

• Assistant Professor, Industrial Engineering University of Arkansas (2017)
• Research Staff Member, IBM Thomas J. Watson Research Center IBM Thomas J. Watson Research Center (2015 — 2017)
• Adjunct Assistant Professor, Industrial and Systems Engineering National University of Singapore (2013 — 2016)
• Research Staff Member, IBM Smarter Cities Research Collaboratory Singapore IBM Research (2012 — 2015)

Research Activity

Area 1: Domain-Aware Data-Driven Methodologies

PDE-based statistical learning for physical/natural/engineering processes:

• sensor-based environmental monitoring (e.g., urban pollution, wildfires, ocean environment), and radar/satellite image modeling (e.g., short-term solar energy prediction, extreme weather events, etc.);
• physics-informed statistical learning for structural dynamics due to aircraft-UAV collisions using the outputs generated by computer models (Finite Element Analysis).

Area 2: Tree-Based Statistical Methods

• Random Forest for recurrent event data, Gradient Boosting for recurrent event data
• Structural gradient boosted trees for medical image analysis (edge detection)

Area 3: Quality and Reliability Engineering

• Stochastic degradation, reliability life testing experiments, condition-based maintenance

Publications

Journal papers: [*student author]

• Forouzannezhad, P., Maes, D., Hippe, D., Thammasorn, P., Iranzad, R., Han, J., Duan, C., Liu, X., Wang, S., Chaovalitwongse, W., Zeng, J., Bowen, S. (2022), Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer, Cancers, Special Issue Medical Imaging and Machine Learning, 14, 1288.
• Liu, X., Yeo, K.M., and Lu, S.Y., (2022), “Statistical Modeling for Spatio-Temporal Data from Physical Convection-Diffusion Processes”, Journal of the American Statistical Association (theory and methods), accepted, arXiv: https://arxiv.org/abs/1910.10375; GitHub code: https://github.com/dnncode/Spatio-Temporal-Model-for-SPDE.
• Liu, X., and Pan, R., (2021), “Boost-R: Gradient Boosting for Recurrent Event Data”, Journal of Quality Technology, Special Issue on Artificial Intelligence & Statistics for Quality Technology, accepted, 53, 545-565, GitHub code: https://github.com/dnncode/Boost-R.
• *Hajiha, M. et al. "A Physics-Regularized Data-Driven Approach for Health Prognostics of Complex Engineered Systems with Dependent Health States", Reliablity Engineering and System Safety, Special Issue on Physics-Informed Machine Learning for Reliability and Safety, 2nd round review.
• 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. GitHub code: https://github.com/dnncode/Random-Forest-for-Recurrence-Data
• (*Ph. D student) Hajiha, M., Liu, X., and Hong, Y. (2021), “Degradation Modeling under Dynamic Operating Conditions and Its Applications”, Journal of Quality Technology, 53,347-348; data: https://github.com/dnncode/LTPP-Data
• Liu, X. (2021), "Statistical Machine Learning -- A Unified Framework", Journal of Quality Technology (*Book Review).
• (*Ph. D student) Iranzad, R., Liu, X., Chaovalitwongse, W. A., Hippe, D. S., Wang, S., Han, J., Thammasorn, P., Duan, C.Y., Zeng, J., Bowen, S. R. (2021+), “Boost-S: Gradient Boosted Trees for Spatial Data and Its Application to FDG-PET Imaging Data”, IISE Transactions on Healthcare Systems Engineering. arXiv: https://arxiv.org/abs/2101.11190
• Liu, X., (2021), “A Simple Procedure for Analyzing Reliability Data from Double-Stage Accelerated Life Tests”, Quality Technology and Quantitative Management, 18, 67-82. GitHub code: https://github.com/dnncode/PDA
• Duan, C., Chaovalitwongse, W. A., Bai, F., Hippe, D., Wang, S., Thammasorn, P., Pierce, L. A., Liu, X., You, J., Miyaoka, R. S., (2020), "Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer", Physics in Medicine & Biology, DOI: 10.1088/1361-6560/abb0c7.
• 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
• 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.
• 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.
• 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. GitHub code: https://github.com/dnncode/STCAR_Radar_Image
• 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.
• 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
• 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.
• 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.
• 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.
• 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.
• Liu, X. (2012), “Planning of Accelerated Life Tests with Dependent Failure Modes Based on a Gamma Frailty Model”, Technometrics, 54, 398-409.
• 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
• 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,
• 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.
• 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.
• Tang, L.C. and Liu, X. (2010), “Planning and Inference for a Sequential Accelerated Life Test”, Journal of Quality Technology, 42(1), 103-118.
• 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.
• 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.
• 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.