Other Previous Research Projects
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Ensemble Tree-Based Method for Recurrence Data
This project was about statistical learning methodologies for the modeling, prediction and optimization of large-scale recurrent event processes with heterogeneous (both static and dynamic) feature information.
Selected publications:
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. Article Link. code. (also on arXiv:1904.01128).
Liu, X., and Pan, R. (2021), "Boost-R: Gradient Boosting for Recurrent Event Data", Journal of Quality Technology, Special Issue-Artificial Intelligence & Statistics for Quality Technology, 53, 545-565. Article Link. code. (also on arXiv:2107.08784).
Health Prognostics of Complex Engineered Systems
This project was about the health prognostics of engineering systems by integrating fundamental system physics and sensor monitoring data (based on real data)
Selected publications:
[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", Reliability Engineering and System Safety, Special Issue--Physics-Informed Machine Learning for Reliability and Safety, 226, 108677. Article Link.
[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. Article Link.
Liu, X., Yeo, K.M. and Kalagnanam, J. (2018), "A Statistical Modeling Approach for Spatio-Temporal Degradation Data", Journal of Quality Technology, 50, 166--182. Special issue on "reliability and maintenance modeling with big data". Article Link. (also available on arXiv:1609.07217).
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). Article Link.
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. Article Link.
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). Article Link.
Liu, X. (2012) "Planning of Accelerated Life Tests with Dependent Failure Modes Based on a Gamma Frailty Model", Technometrics, 54(4), 398-409. Article Link.
Statistical Modeling for Medical Image Data
This project developed the gradient boosting trees for spatially correlated data (Boost.S). The method has been applied to predict FDG-PET (fluorodeoxyglucose-positron emission tomography) imaging data from cancer chemoradiotherapy (*the figure is from the collaborator, Dr. Stephen Bowen's group, from University of Washington).
This project developed Structured Adaptive Boosting Trees algorithm (AdaBoost.S) to surmount the edge detection problem associated with medical images. The method has been applied to detect the multicellular aggregates in fluorescence intravital microscopy (*the figure is from the collaborator, Dr. Margaret Bennewitz's group, from West Virginia University).
Selected publications:
Iranzad, R., Liu, X., Dese, K., Alkhadrawi, H., Snoderly, H., and Bennewitz, M., (2024), "Structured Adaptive Boosting Trees for Detection of Multicellular Aggregates in Fluorescence Intravital Microscopy", Microvascular Research, 156, 104732. Article Link.
Iranzad, R., Liu, X., Chaovalitwongse, W. A., Hippe, D. S., Wang, S., Han, J., Thammasorn, P., Duan, C.Y., Zeng, J., Bowen, S. R. (2022), "Gradient boosted trees for spatial data and its application to medical imaging data", IISE Transactions on Healthcare Systems Engineering, 12, 165-179. Article Link.
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, 25(16), 5027-5037. Article Link.
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. Article Link.
S Bowen, D Hippe, W Chaovalitwongse, P Thammasorn, X Liu, R Iranzad, R Miyaoka, H Vesselle, P Kinahan, R Rengan, J Zeng, (2021), "Voxel Forecast Classifier to Predict Spatially Variant Binary Tumor Voxel Response On Longitudinal FDG-PET/CT Imaging of FLARE-RT Protocol Patients", Medical Physics, 47, E672-E672. Article Link.
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. Article Link.
Other Topics: Stochastic Degradation, Optimal Planning of Reliability Testing, and Condition-Based Maintenance
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