Publications and Presentations

! The papers are for non-commercial research and education use. Not for reproduction, distribution or commercial use.

Journal papers:

1. [student paper] Wei, G.Z., Qiu, F., and Liu, X., “Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks”, Technometrics, accepted (arXiv:2301.00067). 

2. [student paper] Liu, X.C., Phan, D., Hwang, Y., Klein, L. Liu, X., Yeo, K., (2024), "Optimal Sensor Allocation with Multiple Linear Dispersion Processes", under revision.

3. [student paper] Wei, G.Z., Krishnan, V., Xie, Y., Sengupata, M., Zhang, Y.C., Liao, H.T., and Liu, X., (2024), "A Statistical Model for Multi-Source Remote-Sensing Data Streams of Wildfire Aerosols Optimal Depth," INFORMS Journal on Data Science, https://doi.org/10.1287/ijds.2021.0058, arXiv: https://arxiv.org/abs/2206.11766. 

4. [student paper] Iranzad, R., Liu, X. (2024), “A Review of Random-Forests-Based Feature Selection Methods for Data Science Education and Applications”, International Journal of Data Science and Analytics, https://doi.org/10.1007/s41060-024-00509-w.

5. Liu, X., and Yeo, K.M. (2023), “Inverse Model for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes”, Technometrics, 65, 432-445. arXiv: https://arxiv.org/abs/2302.04134. GibHub code: https://github.com/dnncode/inverse-model. 

6. [student paper] Wei, G.Z., Liu, X., and Barton, R. (2023), “An extended PDE-based statistical spatio-temporal model that suppresses the Gibbs phenomenon [pdf]”, Environmetrics, accepted (https://doi.org/10.1002/env.2831)

7. [student paper] Liu, X.C., Liu, X., Kaman, T, Lu, X., and Lin, G. (2023), “Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions [pdf]”, Technometrics, 65, 564-578 (https://doi.org/10.1080/00401706.2023.2203175). Code: https://github.com/Xinchao1995/Physics-Informed-Statistical-Learning-for-Nonlinear-Structural-Dynamics-of-Aircraft-UAV-Collisions. 

8. [student co-author] Liu, X., and Liu, X.C. (2023), “Regression Trees on Grassmann Manifold for Adapting Reduced-Order Models”, AIAA Journal (American Institute of Aeronautics and Astronautics), 61(3), 1318-1333, https://doi.org/10.2514/1.J062180, arXiv: https://arxiv.org/abs/2206.11324. 

9. Liu, X., Yeo, K. M., Lu, S. Y. (2022), “Statistical Modeling for Spatio-Temporal Data from Stochastic Convection-Diffusion Processes [pdf]”, Journal of the American Statistical Association (Theory and Methods), 117, 1482-1499, arXiv: https://arxiv.org/abs/1910.10375; GitHub code: https://github.com/dnncode/Spatio-Temporal-Model-for-SPDE. 

10. 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. 

11. [student author] Hajiha, M., Liu, X., Lee, Y., and Moghaddass, R. (2022) "A Physics-Regularized Data-Driven Approach for Health Prognostics of Complex Engineered Systems with Dependent Health States [pdf]", Reliability Engineering and System Safety, Special Issue--Physics-Informed Machine Learning for Reliability and Safety, 226, 108677.

12. Liu, X., and Pan, R. (2021), "Boost-R: Gradient Boosting for Recurrent Event Data" [pdf], Journal of Quality Technology, Special Issue-Artificial Intelligence & Statistics for Quality Technology, 53, 545-565; code on Github:  https://github.com/dnncode/Boost-R.

13. 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. (arXiv:1904.01128; poster, code on GitHub; data.zip). 

14. [student author] 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, to appear. 

15. Liu, X. (2021+), "Statistical Machine Learning -- A Unified Framework", Journal of Quality Technology, Book Review.

16. [student author] Hajiha, M., Liu, X., and Hong, Y. (2021), “Degradation under Dynamic Operating Conditions: Modeling, Competing Processes and Applications ”, Journal of Quality Technology, 53, 347-368 (data: https://github.com/dnncode/LTPP-Data). 

17. Duan,  C., Chaovalitwongse, W. A., Bai, F., Hippe, D., Wang, S., Thammasorn, P., Pierce, L. A., Liu, X., You, J., Miyaoka, R. S., Vesselle, H.J., Kinahan, P.E., Rengan, R., Zeng, J., and Bowen, S.R. (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.

18. Liu, X., (2020), “A Simple Procedure for Analyzing Reliability Data from Double-Stage Accelerated Life Tests”, Quality Technology and Quantitative Management, to appear code on Github: : https://github.com/dnncode/PDA.  

19. Yeo, K.M., Hwang, Y.D., Liu, X., and Kalagnanam, J. (2019), "Development of hp-inverse model by using generalized polynomial chaos [pdf]", Computer Methods in Applied Mechanics and Engineering, 347, 1-20. (Impact Factor: 4.441; Rank 2 of 103 under MATHEMATICS, INTERDISCIPLINARY APPLICATIONS).

20. Bowen. S, Hippe, D, Chaovalitwongse, W. A., 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, to appear (impact factor: 10.199).

21. Liu, X., Gopal, V., and Kalagnanam, J. (2018), "A Spatio-Temporal Modeling Framework for Weather Radar Image Data in Tropical Southeast Asia [pdf]",  Annals of Applied Statistics, 12(1), 378–407. (*code: https://github.com/dnncode/STCAR_Radar_Image.)  

22. Liu, X., Yeo, K.M. and Kalagnanam, J. (2018),  "A Statistical Modeling Approach for Spatio-Temporal Degradation Data [pdf]", Journal of Quality Technology, 50, 166--182. Special issue on "reliability and maintenance modeling with big data". 

23. 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 [pdf]", Annals of Applied Statistics, 10(2), 756-785.

24. Liu, X. and Tang, L.C. (2016) "Reliability Analysis and Spares Provisioning for Repairable Systems with Dependent Failure Processes and Time-Varying Installed Base [pdf]", IIE Transactions, 48, 43--56. (Featured in Industrial Engineer Magazine Dec 2015). 

25. 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.

26. 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.

27. Liu, X., Al-Khalifa. K., Elsayed, A.E., Coit, D.W, and Hamouda, A.M. (2014) "Criticality Measures for Components with Multi-Dimensional Degradation [pdf]", IIE Transactions, 46, 987–998.

28. Liu, X. and Tang, L.C. (2013) " Planning Accelerated Life Tests with Scheduled Inspections for Log-Location-Scale Distributions [pdf]", IEEE Transactions on Reliability, 62(2), 515 - 526.

29. Liu, X. (2012) "Planning of Accelerated Life Tests with Dependent Failure Modes Based on a Gamma Frailty Model [pdf]", Technometrics, 54(4), 398-409. AMSTATNEWS:  http://magazine.amstat.org/blog/2012/11/01/design-and-model-selection/ 

30. 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 [pdf]", 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)

31. Liu, X.and Tang, L.C. (2012) "Analysis for Reliability Experiments under Blocking [pdf]", Quality Technology and Quantitative Management, Special Issue: Reliability Modeling, Inference and Analysis, 10, 141-160.

32. Liu, X. and Qiu, W.S. (2011) "Modelling and Planning of Step-Stress Accelerated Life Tests with Independent Competing Risks [pdf]", IEEE Transactions on Reliability, 60(4), 712-720. Top accessed article of the journal in Jan 2012 (rank:2)

33. Liu, X. and Tang, L.C. (2010) "Accelerated Life Test Plans for Repairable Systems with Multiple Independent Risks [pdf]", IEEE Transactions on Reliability, 59(1), 115-127. (2010 National Semiconductor Gold Medal, ISE Department, National University of Singapore).

34. Tang, L.C. and Liu, X.(2010) "Planning and Inference for a Sequential Accelerated Life Test [pdf]", Journal of Quality Technology, 42(1), 103-118.

35. Liu, X. and Tang, L.C. (2010) "A Bayesian Optimal Design for Accelerated Degradation Tests [pdf]", Quality and Reliability Engineering International, 26(8), 863-875. Special Issue: Business and Industrial Statistics: Developments and Industrial Practices in Quality and Reliability.

36. Liu, X. and Tang, L.C. (2010) "Planning Sequential Constant-Stress Accelerated Life Tests with Stepwise Loaded Auxiliary Acceleration Factor [pdf]", Journal of Statistical Planning and Inference, 140(7), 1968-1985.

37. 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.

Book Chapter:

Liu, X., and *Hajiha, M., (2022), "A Physics-Regularized Degradation Model for Cooling System Health Management", in Handbook of Smart Energy Systems, Mahdi Fathi, Enrico Zio, Panos Pardalos (Eds.), Springer. 

Tutorials/Short Courses:

1. Analysis of Large Repairable System Reliability Data Sets, 2018 Annual Reliability and Maintainability Symposium, Jan 22, Reno, Nevada.

2. Analysis of Large Repairable System Reliability Data Sets, 2019 Annual Reliability and Maintainability Symposium, Orlando.

3. Analysis of Large Repairable System Reliability Data Sets, 2020 Annual Reliability and Maintainability Symposium, Palm Springs.

Invited talk:

1. "Domain-Aware Statistical Learning for Natural and Engineering Processes", Department of Industrial and Systems Engineering, University of Washington, Apr 2022.

2. “Spatio-Temporal Statistical Models for Physical Convection-Diffusion Processes”, Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Penn State, Oct 2021.

3. Department of Aerospace and Mechanical Engineering, University of Oklahoma, Feb 11, 2021 (virtual).

4. “Physics-Informed Statistical Models for Physical/Engineering Processes”, Department of Industrial and Management Systems Engineering, University of South Florida, Nov 23, 2020 (virtual).

5. “Analysis of Large Heterogeneous Repairable System Reliability Data in Big Data Environment”, IISE DAIS Division, Webinar, Nov, 2018.

6. "Analysis of Large Heterogeneous Repairable System Reliability Data with Covariates in Big Data Environment", Department of Systems Engineering and Engineering Management, City University of Hong Kong, July 2018. 

7. School of Reliability and Systems Engineering, Beihang University, Jun 2018.

8. "Modeling of Spatio-Temporal Data and Some Applications", Department of Mathematical Sciences, J. William Fulbright College of Arts & Sciences, University of Arkansas, Apr 2018.

9. "On the modeling of spatio-temporal data", IE, Peking University, July 2017.

10. "Spatio-Temporal Degradation Models", SSIE, SUNY Binghamton, Apr 2017.

11. "A Statistical Modeling Approach for Spatio-Temporal Data and Its Applications", ISE, Rutgers University, Oct 2016. 

12. "A Statistical Modeling Approach for Spatio-Temporal Data and Its Applications", ISyE, GeorgiaTech, Sep 2016. 

13. "Analytical Models for Condition-Based Maintenance under Dynamic Environments", School of Computer Engineering, Nanyang Technological University, Singapore.

14. "Reliability Modeling and Condition-Based Maintenance," Texas A&M University at Qatar (TAMUQ) and ConocoPhillips Annual Qatar Process Safety Symposium, Doha, Qatar, Mar, 2012. (http://www.gulf-times.com/site/topics/article.asp?cu_no=2&item_no=493326&version=1&template_id=36&parent_id=16)

15. Liu, X, "An Introduction to Condition-Based Maintenance" American Society for Quality (ASQ) Chinese Reliability Webinar Series, Apr. 11, 2012. (http://www.reliabilitycalendar.org/The_Reliability_Calendar/Webinars_-_Chinese/Webinars_-_Chinese.html)

16. "Advances in Modeling, Planning and Analysis of Accelerated Life Tests," Department of Industrial and Systems Engineering, Auburn University, Alabama, USA, Feb 1, 2012.

17. "New Developments in Accelerated Reliability Testing and Spares Provisioning for System Maintenance," Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Jan 31, 2011, Eindhoven, Netherlands.

18. "Planning of Accelerated Life Tests under Non-Destructive Intermittent Inspections," Department of Applied Probability and Statistics, National University of Singapore, Dec 22, 2010, Singapore.