Publications

Selected publications

• Wang, L., Schwedt, T., Chong, C., Wu, T., & Li, J., “KGL Discriminant Subgraph Learning from Functional Brain Sensory Data” IISE Transactions, in press.
• Wang, L., Hawkins-Daarud, A., Swanson, K., Hu, L., & Li, J., “Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine.” IEEE Transactions on Automation Science and Engineering, in press.
• Liu, X., Chen, K., Wu, T., Weidman, D., Lure, F., Li, J., 2021, “A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data.” IISE Transactions, 53(9): 1010-1022.
• Hu, L., Wang, L., …, & Li, J., 2021, “Uncertainty Quantification in the Radiogenomics modeling of EGFR Amplification in Glioblastoma.” Scientific Reports, 11(1), 1-14.
• Zheng, Z., Yan, H., Setzer, F. C., Shi, K. J., Mupparapu, M., & Li, J., 2020, “Anatomically Constrained Deep Learning for Automating Dental CBCT Segmentation and Lesion Detection.” IEEE Transactions on Automation Science and Engineering, 18(2), 603-614.
• Si, B., Schwedt, T, Chong, C., Wu, T., Li, J., 2020, “A Novel Hierarchically-structured Factor Mixture Model for Cluster Discovery from Multi-modality Data.” IISE Transactions, 1-13.
• Wang, K., Patel, B., Wu, T., Zheng, B., & Li, J., 2019, “A Dual-mode Deep Transfer Learning (D2TL) System for Breast Cancer Detection using Contrast Enhanced Digital Mammograms.” IISE Transactions on Healthcare Systems Engineering, 9(4), 357-370.
• Gaw, N., Hawkins-Daarud, A., …, & Li, J., 2019, “Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma using Multiparametric MRI.” Scientific Reports, 9(1), 1-9.
• Yoon, H. & Li, J., 2019, “A Novel Positive Transfer Learning Approach for Telemonitoring of Parkinson’s Disease.” IEEE Transactions on Automation Science and Engineering, 16(1): 180-191.
• Hu, L.S., Yoon, H., Eschbacher, J.M., Baxter, L.C., …, & Li, J., 2019, “Accurate Patient-specific Machine Learning Models of Glioblastoma Invasion using Transfer Learning.” American Journal of Neuroradiology, 40(3): 418-425.
• Liu, X., Fatyga, M., Wu, T., & Li, J., 2019, “Integration of Biological and Statistical Models toward Personalized Radiation Therapy of Cancer.” IISE Transactions, 51(3): 311-321.
• Wang, K., & Li, J., 2018, “Integration of Sparse Singular Vector Decomposition and Statistical Process Control for Traffic Monitoring and Quality of Service Improvement in Mission-Critical Communication Networks.” IISE Transactions, 50(12): 1104-1116.
• Si, B., Dumkrieger, G., Wu, T., Zafonte, R., Dodick, D. W., Schwedt, T. J., & Li, J., 2018, “A Cross-study Analysis for Reproducible Sub-classification of Traumatic Brain Injury.” Frontiers in Neurology, 9: 606.
• Gao, F., Wu, T., Li, J., Zheng, B., Ruan, L., Shang, D., & Patel, B., 2018, “SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.” Computerized Medical Imaging and Graphics, 70: 53-62.
• Si, B., Dumkrieger, G., Wu, T., Zafonte, R., Valadka, A.B., Okonkwo, D.O., Manley, G.T., Wang, L., Dodick, D.W., Schwedt, T.J. and Li, J., 2018, “Sub-classifying Patients with Mild Traumatic Brain Injury: A Clustering Approach based on Baseline Clinical Characteristics and 90-day and 180-day Outcomes.” PloS one, 13(7), p.e0198741.
• Liu, X., Gough, A., Li, J. , 2018, “Semiconductor Corner Lot Generation Robust to Process Variation: Modeling and Analysis”. IISE Transactions, 50(2):126-39.
• Gaw, N., Schwedt, T., Chong, C., Wu, T., Li, J., 2018, “A clinical Decision Support System using Multi-modality Imaging Data for Disease Diagnosis”, IISE Transactions on Healthcare Systems Engineering, 8(1):36-46.
• Liu, X., Chen, K., Wu, T., Weidman, D., Lure, F., Li, J., 2018, “Use of Multi-modality Imaging and Artificial Intelligence for Diagnosis and Prognosis of Early Stages of Alzheimer’s Disease.” Translational Research, 194: 56-67.
• Si, B., Lamb, G., Schmitt, M., & Li, J., 2017, “A Multi-response Multilevel Model with Application in Nurse Care Coordination.” IISE Transactions, 49(7): 669-681.
• Si, B., Yakushev, I., & Li, J., 2017, “A Sequential Tree-based Classifier for Personalized Biomarker Testing of Alzheimer’s Disease Risk.” IISE Transactions on Healthcare Systems Engineering, 7(4): 248-260.
• Ning, S., Byon, E., Wu, T., & Li, J., 2017, “A Sparse Partitioned-Regression Model for Nonlinear System-Environment Interactions.” IISE Transactions, 49(8): 814-826.
• Hu, L.S., Ning, S., Eschbacher, J.M., Baxter, L.C., Gaw, N., …, & Li, J., 2017, “Radiogenomics to Characterize Regional Genetic Heterogeneity in Glioblastoma.” Neuro-Oncology, 19(1): 128-137.
• Wang, K., Zwart, C., Wellness, C., Wu, T., Li, J., 2017, “Integration of Multiple Health Information Systems for Quality Improvement of Radiologic Care”, IISE Transactions on Healthcare Systems Engineering, 7(3): 169-180.
• Schwedt, T., Si, B., Li, J., Wu, T., Chong, C., 2017, “Migraine Subclassification via a Data-Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements”, Headache: The Journal of Head and Face Pain, 57(7): 1051-1064.
• Zou, N., Li, J., 2017, “Modeling and Change Detection of Dynamic Network Data by a Network State Space Model,” IISE Transactions, 49(1): 45-57.
• Chong, C., Gaw, N., Fu, Y., Li, J., Wu, T., Schwedt, T., 2017, “Migraine Classification Using Magnetic Resonance Imaging Resting-State Functional Connectivity Data”, Cephalalgia . 37(9): 828-844.
• Zou, N., Baydogan, M., Zhu, Y., Wang, W., Zhu, J., Li, J., 2015, “A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems,” Technometrics, 55(3):362-373.
• Hu, L. S., Ning, S., Eschbacher, J. M., Gaw, N., Dueck, A. C., Smith, K. A., … & Li, J., 2015, “Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma,” PloS one, 10(11): e0141506.
• Schwedt, T., Chong, C., Gaw, N., Fu, Y., Wu, T., Li, J., 2015, “Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging”, Headache: The Journal of Head and Face Pain, 55(6):762-77.
• Huang, S., Li, J., Lamb, G., Schmitt, M., and Fowler, J., 2014, “Multi-data Fusion for Enterprise Quality Improvement by a Multilevel Latent Response Model,” IISE Transactions, 46(5), 512-525.
• Li, M., Liu, J., Li, J., Kim, B., 2014, “Bayesian Modeling of Multi-state Hierarchical Systems with Multi-level Information Aggregation,” Reliability Engineering & System Safety, 124, 158-164.
• Huang, S., Li, J., Ye, J., Fleisher, A., Chen, K., Wu, T., and Reiman, E., 2013, “A Sparse Structure Learning Algorithm for Bayesian Network Identification from High-Dimensional Data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1328-1342.
• Inman, R.R., Blumenfeld, D.E., Huang, N., Li, J., and Li, J., 2013, “Survey of Recent Advances on the Interface between Production System Design and Quality,” IISE Transactions, 45(6), 557-574.
• Huang, S., Li, J., Chen, K., Wu, T., Ye, J., Wu, X., and Li, Y., 2012, “A Transfer Learning Approach for Network Modeling,” IISE Transactions, 44(11), 1-17.
• Li, J., and Jin, J., 2010, “Optimal Sensor Allocation by Integrating Causal Models and Set-Covering Algorithms,” IISE Transactions, 42(8), 564-576.
• Huang, S., Li, J., Ye, J., Wu, T., Chen, K., Fleisher, A., Reiman, E., “Identifying Alzheimer’s Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis,” NIPS 2011.
• Huang, S., Li, J., Ye, J., Fleisher, A., Chen, K., and Wu, T., Reiman, E., “Brain Effective Connectivity modeling for Alzheimer’s Disease by Sparse Bayesian Network,” KDD 2011.
• Li, J., and Huang, S., 2010, “Regression-based Process Monitoring with Consideration of Measurement Errors,” IISE Transactions, 42(2), 146-160.
• Huang, S., Li, J., Sun, Li., Ye, J., Fleisher, A., Wu, T., Chen, K., and Reiman, E., 2010, “Learning Brain Connectivity of Alzheimer’s Disease by Sparse Inverse Covariance Estimation,” NeuroImage, 50, 935-949.
• Huang, S., Pan, R., and Li, J., 2010, “A Graphical Technique and Penalized Likelihood Method for Identifying and Estimating Infant Failures,” IEEE Transactions on Reliability, 59(4), 650-660.
• Jin, J. and Li, J., 2009, “Multiscale Mapping of Aggregated Signal Features to Embedded Time-Frequency Localized Operations Using Wavelets,” IISE Transactions, 41(7), 615-625.
• Huang, S., Li, J., Sun, Li., Ye, J., Chen, K., and Wu, T., Fleisher, A., and Reiman, E., “Learning Brain Connectivity of Alzheimer’s Disease from Neuroimaging Data,” NIPS 2009.
• Sun, L., Patel, R., Liu, J., Chen, K., Wu, T., Li, J., Reiman, R., and Ye, J., 2009, “Mining Brain Region Connectivity for Alzheimer’s Disease Study via Sparse Inverse Covariance Estimation,” KDD 2009.
• Li, J., Jin, J., and Shi, J., 2008, “Causation-based T² Decomposition for Multivariate Process Monitoring and Diagnosis,” Journal of Quality Technology, 40(1), 46-58.
• Ye, J., Chen, K., Wu, T., Li, J., Zhao, Z., Patel, R., Bai, M., Janardan, R., Liu, H., Alexander, G., and Reiman, E., 2008, “Heterogeneous Data Fusion for Alzheimer’s Disease Study,” KDD 2008
• Li, J., Huang, K.Y., Jin, J., and Shi, J., 2008, “A Survey on Statistical Methods for Health Care Fraud Detection,” Health Care Management Science, 11, 275-287.
• Li, J., Shi, J., and Chang, T.S., 2007, “On-line Seam Detection in Rolling Processes using Snake Projection and Discrete Wavelet Transform,” ASME Transactions, Journal of Manufacturing Science and Engineering, 129(5), 926-933.