Publications and Patents

 Books

  1. Wu, T., Blackhurst, J. (edited), “Managing Supply Chain Risk and Vulnerability – Tools and Methods for Supply Chain Decision Makers”, Springer, 2009.
  2. Ye, N., Wu, T., “Developing Windows-Based and Web-Enabled Information Systems”, CRC Press, Summer, 2014.

 Refereed Publications in Journals: (Symbol * identifies my students, † identifies students from my co-authors, google scholar H-index: 48)

  1. Charlton, J., *Li, T., Wu, T., DeRonde, K., *Xu, Y., Baldelomar, E.J., Bennett, K., “Use of novel structural features to identify urinary biomarkers during acute kidney injury that predict progression to chronic kidney disease”, BMC Nephrology, 2023, https://doi.org/10.1186/s12882-023-03196-0
  2. †Zheng, Z., Su, Y., Chen, K., Weidman, D., Wu, T., Lo, S., Lure, F. & Li, J. “Uncertainty-driven modality selection for data-efficient prediction of Alzheimer’s Disease”, IISE Transactions on Healthcare Systems Engineering, 2023, https://doi.org/10.1080/24725579.2023.2227197.
  3. †Zheng, R., Sun, S., †Liu, H., Wu, T., “Deep Neural Networks-Enabled Vehicle Detection Using High-Resolution Automotive Radar Imaging”, IEEE Transactions on Aerospace and Electronic Systems, 2023, DOI: 10.1109/TAES.2023.3275887.
  4. *Al-Hindawi, F., Soori, T., Hu, H., *Rahman Siddiquee, Yoon, H., Wu, T., Sun, Y., “A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation”, Expert Systems with Applications, 2023, https://doi.org/10.1016/j.eswa.2023.120265
  5. †Mao, L., Li, J., Schwedt, T., Berisha, V., Nikjou, D., Wu, T., Dumkrieger, G., Ross, K., Chong, C., “Questionnaire and Structural Imaging Data Accurately Predict Headache Improvement in Patients with Acute Post-traumatic Headache Attributed to Mild Traumatic Brain Injuries: a Machine-Learning Study”, Cephalalgia, 2023 May;43(5):3331024231172736.
  6. †Ishrak, M.S., *Cai, F., †Islam, S.M., Boric-Lubecke, O., Wu, T., Lubecke, V., “Doppler Radar Remote Sensing of Respiratory Function”, Frontier in Physiology, 2023, https://doi.org/10.3389/fphys.2023.1130478.
  7. *Cai, F., *Patharkar, A., Wu, T., Lure, F., Chen, H., Chen, V., “STRIDE: Systematic Radar Intelligence Analysis for Dementia Risk Evaluation with Gait Signature Simulation and Deep Learning”, IEEE Sensors Journal, 2023, issue 10, 10998-11006, DOI: 10.1109/JSEN.2023.3263071.
  8. †Li, G., Ren, L., †Fu, Y., Yang, Z., Adetola, V., Wen, J., Zhu, Q., Wu, T., Candan, S., O’Neill, Z., “A Critical Review of Cyber-Physical Security for Building Automation Systems”, Annual Reviews in Control, https://doi.org/10.1016/j.arcontrol.2023.02.004
  9. *Sarkar, S., Min, K., Ikram, W., Tatton, R., Riaz, I., Silva, A., Bryce, A., Moore, C., Ho, T., Sonpavde, G., Abdul-Muhsin, A., Singh, P., Wu, T., “Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach”, Cancers, 2023, Mar; 15(6): 1673.  DOI: 3390/cancers15061673.
  10. *Shah, J., *Rahman Siddiquee, M, Krell-Roesch, J., Syrjanen, J.A., Kremers, W.K., Vassilaki, M., Forzani, E., Wu, T, Geda, Y., “Neuropsychiatric symptoms and commonly used biomarkers of Alzheimer’s disease: A literature review from a machine Learning perspective”, Journal of Alzheimer’s Disease, 2023,  92(4):1131-1146. DOI: 10.3233/JAD-221261.
  11. Chong, C., Nikolova, S., Dumkrieger, G., Wu, T., Berisha, V., Li, J., Ross, K., Schwedt, T., “Thalamic subfield iron accumulation after acute mild traumatic injury as a marker of future post-traumatic headache intensity”, The Journal of Head and Face Pain, Jan. 2023, https://doi.org/10.1111/head.14446.
  12. †Yang, J., Wang, S., Wu, T., “Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification”, Applied Intelligence, 53, 1870-1886, 2023.
  13. Nikolova, S., Schwedt, T., Li, J., Wu, T., Dumkrieger, G., Ross, K., Berisha, V., Chong, C., “T2* reduction in patients with acute post-traumatic headache”, Cephalalgia, 2022 doi: 1177/03331024211048509
  14. Nikolova, S., Chong, C., Dumkrieger, G., Li, J., Wu, T., Schwedt, T., “Longitudinal Differences in Iron Deposition in Periaqueductal Gray Matter and Anterior Cingulate Cortex are Associated with Response to Erenumab in Migraine”, Cephalalgia, 2022 doi: 1186/s10194-022-01526-5.
  15. Schwedt, J., Nikolova, S., Dumkrieger, G., Li, J., Wu, T., Chong, C. “Longitudinal Changes in Functional Connectivity and Pain-Induced Brain Activations in Patients with Migraine: A Functional MRI Study Pre- and Post- Treatment with Erenumab”, The Journal of Headache and Pain, 2022, doi: 10.1186/s10194-022-01526-5.
  16. †Chen, Y., Wen, J., †Pradhan, O., Lo, J., Wu, T., “Using Discrete Bayesian Networks for Diagnosing and Isolating Cross-level Faults in HVAC Systems”, Applied Energy, 2022, https://doi.org/10.1016/j.apenergy.2022.120050.
  17. *Rahman Siddiquee, M., *Shah, J., Chong, C., Nikolova, S., Dumkrieger, G., Li, B., Wu, T., Schwedt, T., “Headache Classification and Automatic Biomarker Extraction from Structural MRIs using Deep Learning”, Brain Communications, 2022, https://doi.org/10.1093/braincomms/fcac311.
  18. *Huang, J., Yoon, H., Wu, T., Candan, K.S., Pradhan, O., Wen, J., O’Neill, Z., “Eigen-Entropy: A Metric on Multi-variate Sampling Decision”, Information Sciences, 2022, https://doi.org/10.1016/j.ins.2022.11.023.
  19. *Xu, Y., Wu, T., Charlton, J., Bennett, K., “GAN Training Acceleration Using Fréchet Descriptor-Based Coreset”, Applied Sciences, vol.12, no. 15, 2022, 7599. DOI: https://doi.org/10.3390/app12157599.
  20. *Huang, J., Yoon, H., Pradhan, O., Wu, T., Wen, J., O’Neill, Z., Candan, K.S., “A Cosine-based Correlation Information Entropy Approach for Building Automatic Fault Detection Baseline Construction”, Science and Technology for the Built Environment, 2022, DOI: https://doi.org/10.1080/23744731.2022.2080110.
  21. *Huang, J., Wen, J., Yoon, H., Pradhan, O., Wu, T, O’Neill, Z., Candan, K.S., “Real vs. Simulated: questions on the capability of simulated datasets on building fault detection for energy efficiency from a data-driven perspective”, Energy and Buildings, vol. 259, 2022, 11872.
  22. Rokoni, A., Zhang, L., Soori, T., Hu, H., Wu, T, Sun, Y., “Learning hidden physics from reduced-order analysis of bubble dynamics in boiling heat transfer”, International Journal of Heat and Mass Transfer, 186 (2022), https://doi.org/10.1016/j.ijheatmasstransfer.2021.122501
  23. Rassoulinejad-Mousavi, S.M., *Al-Hindawi, F., Soori, T., Rokoni, A., Yoon, H., Hu, H., Wu, T., Sun, Y., “Deep learning strategies for critical heat flux detection in pool boiling”, Applied Thermal Engineering, vol. 190, 2021, 116849.
  24. *Shah, J., *Gao, F., Li, B., Ghisays, V., Luo, J., Chen, Y., Lee, W., Zhou, Y., Benzinger, T., Reiman, E., Chen, K., Su, Y., Wu, T*, “Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization”, Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 2022, pp.1-10 (*co-corresponding author).
  25. Wu, J., Dong, Q., Zheng, J., Su, Y., Wu, T., Caselli, R., Reiman, E., Ye, J., Lepore, N., Chen, K., Thompson, P.M., Wang, Y., “Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology”, Frontiers in Neuroscience, 2021, vol.15, 2021, 762458.
  26. Lockhart, T., Soangra, R., Yoon, H., Wu, T., Frames, C., Weaver, R., Roberto, K., “Prediction of Fall Risk among Community-Dwelling Older Adults Using a Wearable System”, Scientific Reports, 2021, 11, 20976.
  27. Awada, M., Becerik-Gerber, B., *White, E., Hoque, S., O’Neill, Z., Pedrielli, G., Wen, J., Wu, T., “Occupant Health in Buildings: Impact of the COVID-19 Pandemic on the Opinions of Building Professionals and Implications on Research”, Building and Environment, 2021, vol. 207, 108440.
  28. Wang, W., Wang, S., Geng, Y., Wu, T., Tomovic, M., “An OGI model for personalized estimation of glucose and insulin concentration in plasma”, Mathematical Biosciences and Engineering, Vol. 18, issue 6, pp. 8499-8523, 2021. https://doi.org/10.3934/mbe.2021420
  29. †Pang, Z., Becerik-Gerber, B., Hoque, S., O’Neill, Z., Pedrielli, G., Wen, J., Wu, T., “How Work From Home Has Affected the Occupant’s Well-Being in the Residential Built Environment: An International Survey Amid the COVID-19 Pandemic”, ASME Journal of Engineering for Sustainable Buildings and Cities, 2021, https://doi.org/10.1115/1.4052640.
  30. †Fu, Y., O’Neill, Z., Yang, Z., Adetola, V., Wen, J., Ren, L., Wagner, T., Zhu, Q., Wu, T., “Modeling and Evaluation of Cyber-Attacks on Grid-Interactive Efficient Buildings”, Applied Energy, 2021, vol. 307, 117639.
  31. Chakraborty, S., Wu, T., Forzani, E., Whisner, C., Jackemeyer, D., “Long-term Resting Metabolic Rate Analysis in Pregnancy and Weight Loss Interventions”, International Journal of Prognosis and Health Management,  2021, 12, no. 4, 2021, 3077.
  32. Charlton, J., *Xu, Y., Parvin, N., Wu, T., Gao, F., Baldelomar, E., Morozov, D., Beeman, S., Derakhshan, J., Bennett, K., “Image analysis techniques to map pyramids, pyramid structure, glomerular distribution, and pathology in the intact human kidney from 3D MRI”, American Journal of Physiology-Renal Physiology, 321, 2021, F293–F304.
  33. Chong, C., †Zhang, J., Li, J., Wu, T., Dumkrieger, G., Nikolova, S., Ross, K., Stegmann, G., Liss, J., Schwedt, T., Jayasuriya, S., Berisha, V., “Altered Speech Patterns in Subjects with Post-Traumatic Headache Due to Mild Traumatic Brain Injury”, The Journal of Headache and Pain, 2021, , 22, 82, https://doi.org/10.1186/s10194-021-01296-6
  34. †Wang, W., Wang, S., Wang, X., Liu, D., Geng, Y, Wu, T. “A glucose-insulin mixture model and its application to nocturnal hypoglycemia prediction”, IEEE Transactions on Biomedical Engineering, 2021 Mar;68(3):834-845. doi: 10.1109/TBME.2020.3015199.
  35. †Si, B., Schwedt, T., Chong, C., Wu, T., Li, J. “A novel hierarchically-structured factor mixture model for cluster discovery from multi-modality data”, IISE Transactions, Volume 53,  issue 7, pp. 799-811, 2021.
  36. Hu, L., Wang, L., Hawkins-Daarud, A., Eschbacher, M., Singleton, K., Jackson, P., Swanson, K., Sereduk, C., Peng, S., Wang, P., Wang, J., Baxter, L., Smith, K, Mazza, G., Stokes, A., Bendok, B., Zimmerman, R., Kristhna, C., Porter, A., Mrugala, M., Hoxworth, J., Wu, T., Tran, N., Swanson, K., Li, J., “Uncertainty Quantification in Radiogenomics: EGFR Amplification in Glioblastoma”, Scientific Reports, 11, Article number: 3932 , 2021.
  37. Charlton, J., *Xu, Y., Wu, T., deRonde, K., Hughes, J., L., Dutta, S., Oxley, G.T., Cwiek, A., Cathro, H.P., Charlton, N.P., Conaway, M., Baldelomar, E., Parvin, N., Bennett, K., “Magnetic resonance imaging accurately tracks kidney pathology and heterogeneity in the transition from acute kidney injury to chronic kidney disease”, Kidney International, Vol. 99, Issue 1, Jan. 2021, pp. 173-185.
  38. *Fu, Y., Wu, T., “Gaussian Mixture Models with Feature Selection: An Embedded Approach”, Computers & Industrial Engineering, Volume 152, February 2021, 107000, (https://doi.org/10.1016/j.cie.2020.107000)
  39. Awada, M., Becerik-Gerber, B., Hoque, S., O’Neill, Z., Pedrielli, G., Wen, J., Wu, T., “Ten questions concerning occupant health in buildings during normal operations and extreme events including the COVID-19 pandemic”, Building and Environment, Volume 188, 15 January 2021, 107480, https://doi.org/10.1016/j.buildenv.2020.107480
  40. †Liu, X., Chen, K., Weidman, D., Wu, T., Lure, F., Li, J., “A novel transfer learning model for predictive analytics using incomplete multimodality data”, IISE Transactions, 2020, available at https://doi.org/10.1080/24725854.2020.1798569
  41. Talmage, C., Knopf, R., Wu, T., Winkel, D., Mirchandani, P., Candan, K., “Decreasing Loneliness and Social Disconnectedness among Community-Dwelling Older Adults: The Potential of Information and Communication Technologies and Ride-Hailing Services”, Journal of Activities, Adaptation & Aging, 2020, available at https://doi.org/10.1080/01924788.2020.1724584
  42. *Xu, Y., Wu, T., *Gao, F., Charlton, J., Bennett, K., “Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis”, Scientific Report, Vol. 10, article number: 326, available at https://www.nature.com/articles/s41598-019-57223-y, 2020.
  43. *Xu, Y., Wu, T., Charlton, J., *Gao, F., Bennett, K., “Small Blob Detector Using Bi-threshold Constrained Adaptive Scales”, IEEE Transactions on Biomedical Engineering, 2020, DOI: 10.1109/TBME.2020.3046252
  44. *Gao, F., Yoon, H., *Xu, Y., Goradia, D., Luo, J., Wu, T., Su, Y., “AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction”, NeuroImaging: Clinical, 27, https://doi.org/10.1016/j.nicl.2020.102290, 2020.
  45. *Gao, F., Wu, T., Chu, X, Yoon, H., Xu, Y., Patel, B., “Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis”, IEEE J Biomed Health Informatics, Vol. 24, issue 1, pp. 39-49, doi:10.1109/JBHI.2019.2912659, 2020.
  46. Qin, Q., Xie, K., He, H., Li, L., Chu, X., Wu, T., “An effective and robust decomposition – ensemble energy price forecasting paradigm with local linear prediction”, Energy Economics, 83: 402-414, 2019.
  47. †Wang, K., Patel, B., Wang, L., Wu, T., Zheng, B., Li, T., “A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms”, IISE Transactions on Healthcare Systems Engineering, Vol. 9, issue 4, 2019, pp. 357-370,
  48. *Gao, F., Yoon, H., Wu, T., Chu, X., A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging. Expert Systems with Applications, Vol 143, https://doi.org/10.1016/j.eswa.2019.112957, 2020.
  49. *Su, C., Weir, J., Zhang, F., Yan, H., Wu, T., “ENTRNA: A Framework to Predict RNA Foldability”, BMC BioInformatics, 20, 373, 2019.
  50. † Gaw, N., Hawkins-Daarud, A., Hu, L. S., Yoon, H., Wang, L., Xu, Y., Jackson, P. R., Singleton, K., Baxter, L. C., Eschbacher, J., Gonzales, A., Nespodzany, A., Smith, K., Nakaji, P., Mitchell, J. R., Wu,T., Swanson, K. R. and Li, J., “Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma using Multiparametric MRI”,  Scientific Reports, https://www.nature.com/articles/s41598-019-46296-4, 2019.
  51. Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J. “Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.”, AJNR Am J Neuroradiol.40(3):418-425. doi: 10.3174/ajnr.A5981, 2019.
  52. †Liu, X., Fatyga, M., Wu, T., Li, J., “Integration of Biological and Statistical Models toward Personalized Radiation Therapy of Cancer”, IISE Transactions, Vol 51, issue 3, pp. 311-321, 2019.
  53. *Gao, F., Wu, T., Li, J., Zheng, B., Ruan, L. Shang, D., Patel, B., “SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis”, Computerized Medical Imaging and Graphics, 70, 53-62, 2018.
  54. Chu, X.H., Wu, T., Weir, J., Shi, Y.H., Li, Li, “Learning–interaction–diversification framework for swarm intelligence optimizers: a unified perspective”, Neural Computing and Applications, Aug., 2018, available at DOI: 10.1007/s00521-018-3657-0
  55. Kriegshauser, S., Paden, R.G., *He, M., Humphreys, M., Zell, S., *Fu, Y., Wu, T., Sugi, M., Silva, C., “Rapid kV-switching single-source dual-energy CT ex vivo renal calculi characterization using a multiparametric approach: refining parameters on an expanded dataset”, Abdominal Radiology, Vol. 43, issue 6, pp. 1439-1445, 2018.
  56. Danala, G., Patel, B., Aghaei, F., Heidari, M., Li, J., Wu, T., Zheng, B., “Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms”, Annals of Biomedical Engineering, 2018, available at https://doi.org/10.1007/s10439-018-2044-4
  57. †Liu X, Chen K, Wu T, Weidman D, Lure F, Li J. Use of Multi-modality Imaging and Artificial Intelligence for Diagnosis and Prognosis of Early Stages of Alzheimer’s Disease. Translational Research. 2018. Vol 194, pp. 56-67.
  58. †Si, B., †Dumkrieger, G., Wu, T., Zafonte, R., Dodick, D., Schwedt, T., Li, J., “A cross-study analysis for reproducible sub-classification of Traumatic Brain Injury”, Frontier Neurology, Aug., 2018, available at https://doi.org/10.3389/fneur.2018.00606
  59. Patel, B., Ranjbar, S., Wu, T., Pockaj, B.A., Li, J., Zhang, N., Lobbes, M., Zhang, B., Mitchell, R., “Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study”, European Journal of Radiology, Vol. 98, Jan. 2018, pp.207-213.
  60. Ranjbar, S., †Ning, S., Zwart, C., Wood, C., Weinding, S., Wu, T., Mitchell, R., Li, J., Hoxworth, J., “Computed Tomography Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma”, Journal of Computer Assisted Tomography, 2018 Mar/Apr;42(2):299-305. doi: 10.1097/RCT.0000000000000682.
  61. Gaw, N., Schwedt, T., Chong, C., Wu, T., Li., J., “A clinical Decision Support System using Multi-modality Imaging Data for Disease Diagnosis”, IIE Transactions on Healthcare Systems Engineering, Vol. 8, issue 1, pp. 36-46, 2018.
  62. Schwedt, T., †Si., B., Li, J., Wu, T., Chong, CD., “Migraine Subclassification via a Data Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measures”, Headache, 2017, June 19. Doi: 10.1111/head.13121.
  63. Niu, B., Liu, J., Wu, T., Chu, X., Wang, Z.X., Liu, Y.M., “Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018 Nov-Dec;15(6):1865-1876. doi: 10.1109/TCBB.2017.2742946. Epub 2017 Aug 29.
  64. DeLeon, T., Borad, M., Wu, T., Li, J., Zwart, C., Yang, G., Holeman, S., Silva, A., “Utility of CT texture for identification of FGFR2 fusion in cholangiocarcinoma: A radiogenomic pilot study”, May 2017, Journal of Clinical Oncology 35(15_suppl): e15626-e15626, DOI: 10.1200/JCO.2017.35.15_suppl.e15626
  65. †Ramkumar, S., †Ranjbar, S., †Ning, S., Lal, D., Zwart, CM., Wood, CP., Weindling, SM., Wu, T., Mitchell, JR., Li, J., Hoxworth, JM., “MRI-based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma”, American Journal of Neruoradiology, 2017, Mar. 2, doi: 10.3174/anjr.A5106.
  66. †Wang, K., Zwart, C., Wellness, C., Wu, T., Li., J., “Integration of Multiple Health Information Systems for Quality Improvement of Radiologic Care”, IIE Transactions on Healthcare Systems Engineering, Vol. 7, issue 3, pp. 169-180, 2017.
  67. † Ning, S., Byon, E., Wu, T., Li, J. “A Sparse Partitioned-Regression Model for Nonlinear System-Environment Interactions”, IIE Transactions, Vol. 49, No. 8, pp. 814-826, 2017.
  68. *Zhang, M., Wu, T., Beeman, SC, Cullen-McEwen L., Bertram JF, Charlton, JR, Baldelomar, “Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information”, IEEE Transactions on Medical Imaging, 2016, 35 (4), 1127-37, doi: 10.1109/TMI.2015.2509463. Epub 2015 Dec 17.
  69. †Liu, X., Li, J., Wu, T., Schild, S. E., Schild, M. H., Wong, W., Vora, S., Fatyga, M.  “Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers.” OMICS Journal of Radiology, 5(3), 2016.
  70. Hu, L., †Ning, S., Eschbacher, J., Baxter, L., *Gaw, N, *Ranjbar, S., Plasencia, J., Dueck, A., Peng, S., Smith, K, Nakaji, P., Karis, J., Quarles, C., Wu, T., Loftus, J., Jenkins, R., Sicotte, H., Kollmeyer, T., O’Neill, B., Elmquist, W., Hoxworth, J., Frakes, D., Sarkaria, J., Swanson, K., Tran, N., Li, J., Mitchell, J., “Radiogenomics to characterize regional genetic heterogeneuity in glioblastoma”, Neuro-Oncology, 2016, doi:10.1093/neuonc/now135
  71. Chong, C., †Gaw, N., *Fu, Y., Li, J., Wu, T., Schwedt, TJ., “Migraine classification using magnetic resonance imaging resting-state functional connectivity data”, Cephalagia, June 15, 2016, pii: 0333102416652091 (2016 Harold Wolff-John Graham Award Paper)
  72. *Cui, C., Wu, T., Hu, M., Weir, J., Li, X., “Short-Term Building Energy Model Recommendation System: A Meta-Learning Approach” Applied Energy, (2016), pp. 251-263 DOI information: 10.1016/j.apenergy.2016.03.112
  73. Kriegshauser, J.S., Silva, Al., Paden, R.B., *He, M., Humphreys, M., Zell, S., *Fu, Y., Wu, T., “Ex Vivo Renal Stone Characterization with Single-Source Dual-Energy Computed Tomography: A Multiparametric Approach”, Academic Radiology, 2016 Aug;23(8):969-76. doi: 10.1016/j.acra.2016.03.009. Epub 2016 May 17.
  74. *Cui, C., Hu, M., Weir, J., Wu, T., “A Recommendation System for Meta-Modeling: A Meta-Learning based Approach”, Expert Systems with Applications, Vol. 46, pp. 33-44, March 2016.
  75. *Worger, D., *Jalao, E., Wirthlin, R., Colombi, J., Wu, T., “Intervention Strategies for the Department of Defense Acquisition Process”, the Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, Vol. 13(2) 139-151, 2016.
  76. Hu, LS, †Ning, S., Eschbacher, JM, Gaw, N., Dueck, AC, Smith, KA, Nakaji, P, Plasencia, J., Ranjbar, S., Price, SJ, Tran, N, Loftus, J, Jenkins, R, O’Neill, BP, Elmguist, W, Baxter, LC, *Gao F., Frakes, D, Karis, JP, Zwart, C, Swanson, KR, Sarkaria, J, Wu, T, Mitchell, R, Li, J, “Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma”, PloS One, 2015, Nov. 24, 10 (11), e0141506, doi: 10.137/journal.pone.0141506.
  77. Baldelomar, E.J., Charlton, J.R., Beeman, S.C., Cullen-Mcewen, L., Pearl, V.M., Bertram, J.F., Wu, T., *Zhang, M., Bennett, K.M., “Phenotyping by magnetic resonance imaging nondestructively measures glomerular number and volume distribution in mice with and without nephron reduction”, Kidney International, 2015, Nov 4. Doi: 10.1038/ki.2015.316 (impact factor: 8.563)
  78. *Zhang, M, Wellnitz, C., Cui, C., Pavlicek, W., Wu, T., “Automated Detection of z-axis coverage with abdomen-pelvis computed tomography examinations”, Journal of Digital Imaging, 2015, June 28 (3): 362-7, doi: 10.1007/s10278-014-9743-7.
  79. †Cui, X., Zhao, Y., Lim, K., Wu, T., “Perspective projection model for prism-based stereovision”, Optics Express, 23 (21), 27542-27557, 2015.
  80. *He, M., Wu, T., Silva, A., Zhao, Y., Qian, W., “Augmenting cost-SVM with Gaussian Mixture Models for Imbalanced Classification”, Artificial Intelligence Research, Vol 4, No. 2, 2015, DOI: 10.5430/air.v4n2p93.
  81. *He, M., Wu, T., Silva, A., Zhao, Y., Qian, W., “K Nearest Gaussian – A Model Fusion based Framework for Imbalanced Classification with Noisy Dataset”, Artificial Intelligence Research, Vol 4, No. 2, 2015, DOI:http://dx.doi.org/10.5430/air.v4n2p126
  82. Schwedt, T., Chong, C., Wu, T., †Gaw N., *Fu, Y., Li, J., “Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging”, Headache, 2015, June 55(6):762-77, DOI: 10.1111/head.12584 (2015 Harold G. Wolff Award Paper)
  83. Zwart, C.M., *He, M., Wu, T., Demaerschalk, B.M., Mitchell, J.R., Hara, A.K., “Selection and Pilot Implementation of a Mobile Image Viewer: A Case Study”, JMIR mHealth uHealth, 2015; 3(2): e45, DOI: 10.2196/mhealth.4271
  84. *Silva, A., Grimm R., Glaser K., *Fu, Y., Wu, T., Ehman R., Silva, C., “Magnetic Resonance Elastography: Evaluation of New Inversion Algorithm and Quantitative Analysis Method”, Abdominal Imaging, April, 2015, 40 (4): 810-7. doi: 10.1007/s00261-015-0372-5.
  85. *Zhang, M., Oldan J., *He, M., Wu, T., Silva, A., Li, J., Mitchell, R., Pavlicek W., Roarke M., Hara, A., “Pilot Study: Two-Stage Hybrid Model to Correlate Single Energy CT and PET in Pancreatic Adenocarcinoma”, Journal of Health Medical Informatics, 5:175, doi: 10.4172/2157-7420.1000175, 2015.
  86. *Zhang, M., Wu, T., Bennett, K., “Small Blob Identification in Medical Imaging Using Regional Features from Optimum Scale”, IEEE Transactions on Biomedical Engineering, , Vo. 62, No. 4, April 2015.
  87. Dang, M., Lysack, J., Wu, T., Matthews, TW., Chandarana, SP., Brockton, NT, Bose, P., Bansal, G., Cheng, H., Mitchell, JR, Dort, JC., “MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma”, American Journal of NeuroRadiology, 2015 Jan;36(1):166-70. doi: 10.3174/ajnr.A4110. Epub 2014 Sep 25.
  88. Oldan, J., *He, M., Wu, T., Silva, A., Li, J., Mitchell, R., Pavlicek, W., Roarke, M., Hara, A., “Pilot Study: Evaluation of Dual-Energy Computed Tomography Measurement Strategies for Positron Emission Tomography Correlation in Pancreatic Adenocarcinoma”, Journal of Digital Imaging, 2014 Dec; 27(6): 824–832
  89. †Beeman, S. C., Cullen-McEwen, L.A., Puelles, V.G., *Zhang, M., Wu, T., Baldelomar, E.J., Dowling, J., Charlton, J.R., Forbes, Ng, A., Wu, Q-Z., Armitage, A., Egan, G., Bertram, J., Bennett, K., “MRI-based glomerular morphology and pathology in whole human kidneys”, American Journal of Physiology – Renal Physiology, June 2014, Vol. 306, No. 11, F1381-F1390, doi: 10.1152/ajprenal.00092.2014.
  90. Sun, Q., Zheng, B., Lure, F., Wu, T., Zhang J., Wang, B. Y., Saltzstein, E.C., Qian, W., “Prediction of Near-term Risk of Developing Breast Cancer Using Computerized Features from Bilateral Mammograms”, Computerized Medical Imaging and Graphics, 2014, doi: http://dx.doi.org/10.1016/j.compmediimag.2014.03.001
  91. *Jalao, E. R., Wu, T., Shunk, D., “A Stochastic AHP Decision Making Methodology for Imprecise Preferences”, Information Science, 2014, Vo. 270, pp.192-203.
  92. *Chu, X., *Hu, M., Wu, T., Weir, J.D., Lu, Q., “AHPS2: An optimizer using adaptive heterogeneous particle swarms”, Information Sciences, vol. 280, pp. 26-52, 2014.
  93. *Li, F, Wu, T., Hu, M., “Design of a Decentralized Framework for Collaborative Product Design Using Memetic Algorithms”, Optimization and Engineering, September 2014, Vol. 15, No. 3, pp. 657-676.
  94. *Hu, M., Wu, T., Weir, J.D., “An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 705-720, 2013.
  95. †Huang, S., Li, J., Ye, J., Fleisher, A., Chen, K., Wu, T., Reiman, E., “A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 6, pp. 1328-1342, 2013.
  96. *Li, F., Wu, T., Badiru, A., *Hu, M., Soni, S., “A single loop deterministic method for reliability based design optimization”, Engineering Optimization, Vol. 45, No. 4, pp. 435-458, 2013.
  97. Yue, S.H., Wu, T., Pan, J., Wang, H.X., “Fuzzy Clustering Based ET Image Fusion”, Information Fusion, DOI10.1016/2012.09.004, 2012.
  98. †Knodadadegan, Y., Zhang, M., Pavlicek, W., Paden, R.G., Chong, B., Huettl, E., Schueler, B., Fetterly, K., Langer, S., Wu, T., “Validation and Initial Clinical Use of Automatic Peak Skin Dose Localization with Fluoroscopic and Interventional Procedures”, Journal of Radiology, 266: 246-255, 2012 (impact factor: 5.726, senior author).
  99. *Hu, M, Wu, T., Weir, J., “An Intelligent augmentation of particle swarm optimization with multiple adaptive methods”, Information Science, vol. 213, pp. 68-83, 2012.
  100. *Hu, M., Weir, J.D., and Wu, T., “Decentralized Operation Strategies for an Integrated Building Energy System using a Memetic Algorithm”, European Journal of Operational Research, vol. 217, pp. 185-197, 2012.
  101. *Jalao, E., Shunk D., Wu, T., “Life Cycle Costs and the Analytic Network Process for Software-as-a-Service Migration”, International Journal of Computer Science, Vol. 39, No. 3, pp. 269-275, 2012.
  102. Wu, T., Huang, S., Blackhurst, J., *Zhang, X.L., *Wang, S.S., “Supply Chain Risk Management: An Agent-Based Simulation to Study the Impact of Retail Stockouts”, IEEE Transactions on Engineering Management, DOI: 0.1109/TEM.2012.2190986, 2012.
  103. †Huang, S., Li, J., Chen, K., Wu, T., Ye, J., Wu, X., and Li, Y. “A Transfer Learning Approach for Network Modeling”, IIE Transactions, vol. 44, no. 11, pp. 915-931, 2012.
  104. Wu, T., *Bae, M., *Zhang, M., Pan, R., Badea, A., “A prior feature SVM-MRF based method for mouse brain segmentation”, NeuroImage, 59 (3), pp. 2298-2306, 2012 (impact factor: 5.457)
  105. *Wang, S., Wu, T., *Weng S-J., Fowler, J., “A Control Chart Based Approach to Monitoring Supply Network Dynamics using Kalman Filtering”, International Journal of Production Research, vol. 50, No. 11, pp.3137-3151, 2012.
  106. *Weng, S-J., Wu, T., Mackulak, G., Verdini, W., “A Multi-Tool Integrated Methodology for Distributed Resource Allocation in Healthcare”, International Journal of Industrial and Systems Engineering, vol. 11, No. 4, pp. 428-452, 2012.
  107. Li, V.C., Wu, T., †Ding, M.G., “Inbound Supply Chain Risk Analysis”, Transportation Planning Journal, Vol. 40, No. 2, pp. 185-212, June 2011.
  108. Yue, S., Wu, T., Liu, Z., Zhao, X., “Fused Multi-Characteristics Validity Index: An Application to Reconstructed Image Evaluation in Electrical Tomography”, International Journal of Computational Intelligence Systems, vol. 4 no. 5, pp. 1052-1061, 2011.
  109. Wu, T., Soni, S., *Hu, M., *Li, F., Badiru, A., “The Application of Memetic Algorithms for Forearm Crutch Design: A Case Study”, Mathematical Problems in Engineering, doi: 10.1155/2011/162580, 2011.
  110. †Beeman, S., *Zhang, M., Gubhaju, L., Wu, T., Bertram, J., Frakes, D., Cherry, B., Bennett, K.,” Measuring glomerular number and size in perfused kidneys using MRI”, American Journal of Physiology-Renal Physiology, pp.1454-1457, 300(6), 2011 (impact factor: 4.17).
  111. *Hu, M., Pavlicek, W., Liu, P. T., Zhang, M., Langer, S., *Wang, S., Place V., Miranda, R., Wu, T., “Efficiency Metrics for Imaging Device Productivity”, Radiographics, pp. 603-616, 31 (2), 2011 (impact factor: 2.747, senior author).
  112. †Khodadadegen, Y., Zhang, M., Pavlicek P., Robert, P., Chong, B., Schueler, B., Fetterly, K., Langer, S., Wu, T., “Automatic Monitoring of Localized Skin Dose with Fluoroscopic and Interventional Procedure”, Journal of Digital Imaging, pp. 626-639, 24 (4), 2011, (impact factor:255, senior author)
  113. *Wang, S., Pavlicek, W., Roberts, C., Langer, S., Zhang, M., *Hu, M., Mornin, R., Schueler, B., Wellnitz, C., Wu, T., “An Automated DICOM Database Capable of Arbitrary Data Mining (including Radiation Dose indicators) for Quality Monitoring, Journal of Digital Imaging, Vol. 24, issue 2, 2011, pp. 223-233 (impact factor: 1.255, senior author)
  114. *Zhang, X.L., Lu, Q., Wu, T., “Petri-Net Based Applications for Supply Chain Management: An Overview”, International Journal of Production Research, Vol. 49, No. 13, pp. 3939-3961, 2011.
  115. Xia, L., Ying, W., Dong, J., Wu, T., Xie, M., Zhao, Y. “A Hybrid Nested Partitions Algorithms for Facility Location Optimization Problems”, IEEE Transactions on Automation, Science and Engineer, Vol. 7, Issue 3, pp. 654-658, 2010.
  116. †Huang, S., Li, J., †Sun, L., Ye, J., Fleisher, A., Wu, T., Chen, K., Reiman, E., “Learning Brain Connectivity of Alzheimer Disease by Sparse Inverse Covariance Estimation” NeuroImage, 50, 935-949, 2010. (impact factor: 5.457)
  117. *Bae, M., Wu, T., Pan, R., “Mix-Ratio Sampling: Classifying Multiclass Imbalanced Mouse Brain Images Using Support Vector Machine”, Expert Systems with Applications, Vol. 37, Issue 7, 4955-4965, 2010.
  118. Yue, S., Wang, J-S., Wu, T., Wang, H., “A new separation measure for improving the effectiveness of validity indices”, Information Sciences, Vol. 180, Issue 5, 748-764, 2010.
  119. *Li, F., Wu, T., *Hu, M., Dong, J., “An accurate penalty based approach for reliability based design optimization”, Journal of Research in Engineering Design, Vol. 21, No. 2, 87-98, 2010.
  120. *Parmar, D., Wu, T., Callarman, T., Fowler, J., Wolfe, P., “A Clustering Algorithm for Supplier Base Management”, International Journal of Production Research, Vol. 48, Issue 13, 3803-3821, 2010.
  121. *Feller, A., Wu, T., Shunk, D., Fowler, J. “Supply Chain Management Model Translation and Analysis – an Integrated SCM Multi-Paradigm Modeling Framework”, IEEE Transactions on System, Man, Cybernetics Part A., 1022-1034, 2009.
  122. *Bae, M., Pan, R., Wu, T., Badea, A., “Automated Segmentation of Mouse Brain Images Using extended MRF”, NeuroImage, 46, 717-725, 2009 (Impact Factor: 5.457)
  123. *Weng, S-J, Wu, T., Blackhurst, J., Mackulak, G., “An Extended DEA Model for Hospital Department Performance Evaluation and Improvement”, Health Services and Outcomes Research Methodology, Vol. 9, Issue 1, 39-45, 2009.
  124. Wu, T., and Blackhurst, J., “Supplier Evaluation and Selection: An Augmented DEA Approach”, International Journal of Production Research, Vol. 47, No. 16, 4593-4608, 2009.
  125. *Ganguly, S., Wu, T. and Blackhurst, J., “A Price-Based Negotiation Mechanism for Distributed Collaborative Design”, IEEE Transaction on Engineering Management, vol. 55, No. 3, 496-507, 2008.
  126. Blackhurst, J., Wu, T., Craighead, C. W., “A Systematic Approach for Predicting Supply Chain Conflicts”, OMEGA, the International Journal of Management Science, Vol. 36, 680-696, 2008.
  127. Blackhurst, J., Wu, T., and O’Grady, P. “A Network Based Decision Tool to Model Uncertainty in Supply Chain Operations”, Production Planning and Control, Vol. 18, No. 6, 526-535 (10), 2007.
  128. *Parmar, D., Wu, T., Blackhurst, J., “MMR: An Algorithm for Clustering Categorical Data Using Rough Set Theory”, Data and Knowledge Engineering, Vol. 63, Issue 3, 879-893, 2007.
  129. Kwon, Y-J, Wu, T., “Cognitive Understanding of Remote Systems from the Perspectives of Online Laboratory Learning”, ASEE: Computers in Education Journal, Vol. XVII, No.3, 93-105, 2007.
  130. Bayraktar, E., Jothishankar, M.C., Wu, T., “Evolution of Operations Management”, Management Research News, Vol. 30, No. 11, 843-871, 2007.
  131. Wu, T., Blackhurst, J., Shunk, D., *Appalla, R., “AIDEA: A Methodology for Supplier Evaluation and Selection in a Supplier-Based Manufacturing Environment”, International Journal of Manufacturing Technology and Management, Vol. 11, No. 2, 174-192, 2007.
  132. Wu, T., Blackhurst, J., and O’Grady, P., “A Methodology for Supply Chain Disruption Analysis”, International Journal of Production Research, Vol. 45, No. 7, 1665-1682, 2007.
  133. Wu, T., Blackhurst, J., *Chidambaram, V., “A Model for Inbound Supply Risk Analysis”, Computers in Industry, Vol. 57, Issue 4, 350-365, 2006.
  134. *Ganguly, S., Wu, T., “A Principle-Agent Model For Distributed, Collaborative Design Negotiation”, Journal of Design and Process Science: Transactions of the SDPS, Vol. 9, No. 2, 65-74, 2005.
  135. Wu, T., Blackhurst, J. “A Modeling Methodology for Supply Chain Synthesis and Disruption Analysis”, International Journal of Knowledge-Based & Intelligent Engineering System, 9, No. 2, 93-106, 2005.
  136. Wu, T., O’Grady, P., “A Network Based Approach to Integrated Supply Chain Design”, Production Planning and Control, Vol. 16, No. 5, 444-453, July 2005.
  137. Kwon, Y.J., Wu, T., “Real-time Machining Quality Inspection for Design for Manufacturing”, Journal of Advanced Manufacturing Systems, Vol. 4, No. 1, pp. 21- 36, 2005.
  138. Blackhurst, J., Wu, T., and O’Grady, P., “Petri Net based Decision Support for Supply Chain, Product and Process Design Decisions”, Journal of Operations Management, Vol. 23, Issues 3-4, 325-343, April 2005.
  139. Wu, T., Ye, N. and *Zhang, D., “Comparison of Distributed Methods for Resource Allocation”, International Journal of Production Research, Vol. 43, No. 3, 515-536, 2005.
  140. Wu, T., *Xie, N., Blackhurst, J., “Design and Implementation of Distributed Information System for Collaborative Product Development”, ASME Transactions: Journal of Computing and Information Science in Engineering, Vol. 4, No. 4, 281-293, 2004.
  141. Tseng, T-L, Jothishankar, M. C. and Wu, T., “Quality Control Problem in Printed Circuit Manufacturing – a Rough Set Based Approach”, Journal of Manufacturing Systems, Vol. 23, No. 1, 56-72, 2004.
  142. Kwon, Y.J., Wu, T., Saldivar, J.O., “SMWA: A CAD-Based Decision Support System for the Efficient Design of Welding”, International Journal of Concurrent Engineering: Research and Application, 12, 295-304, 2004.
  143. Wu, T., O’Grady, “A Methodology for Improving the Design of a Supply Chain” International Journal of Computer Integrated Manufacturing, Vol. 17, No. 4, 281-293, 2004.
  144. Jothishankar, M. C., Wu, T., Johnie, R. and *Shiau, J-Y., “Case Study: Applying Data Mining to Defect Diagnosis”, Journal of Advanced Manufacturing Systems, Vol. 3, No. 1, 69-83, 2004.
  145. Wu, T., O’Grady, “An extended Kalman Filter for Collaborative Supply Chains”, International Journal of Production Research, Vol. 42, No. 12, 2457-2475, 15, 2004.
  146. Blackhurst, J., Wu, T., and O’Grady, P., “A Network-based approach to modeling uncertainty in a supply chain”, International Journal of Production Research, Vol. 42, No. 8/15, 1639-1658, 2004.
  147. Wu, T., O’Grady, “A Network Based Approach to Integrated Enterprise Concurrent Engineering”, Journal of Advanced Manufacturing Systems, Vol. 2, No. 2, 187-200, 2003
  148. Wu, T., O’Grady, P., “Analyzing Large-scale Imprecise Concurrent Engineering Systems”, Journal of Chinese Institute of Industrial Engineers, Vol. 20, No.3, 203-216, 2003.
  149. Wu, T., Blackhurst, J., and O’Grady, P., “Integrated Enterprise Concurrent Engineering: A Framework and Implementation”, International Journal of Concurrent Engineering: Research and Applications, Vol. 9, No. 2, 211-222, 2001.
  150. Wu, T., O’Grady, P., “Trans: A System for Integrated Supply Chain Design”, International Journal of Agile Manufacturing, Vol. 3, Issue 2, 21-36, 2000.
  151. Wu, T., O’Grady, P., “A Concurrent Engineering Approach to Design for Assembly”, International Journal of Concurrent Engineering: Research and Applications, Vol. 7, No. 3, 231-244, 1999.

Patents

  1. Patent No: US9,098,876, Facilitating revenue generation from wholesale electricity markets using an engineering-based energy asset model, Alan Steven, Eunice Hameyie, Jin Wen, Teresa Wu, Ajay Sunder.
  2. Patent No: US10,045,728, Kidney Glomeruli Measurement Systems and Methods, Teresa Wu, Min Zhang
  3. Patent No: US 10,909,675, Multiple texture analysis and ML algorithms to analyze multi-parametric MRI, Leland Hu, …, Teresa Wu, et. al.
  4. Patent No: US 2020/0342359 A1, Methods for Using Machine Learning and Mechanistic Models for Biological Feature Mapping with Multiparametric MRI, LS Hu, J Li, KR Swanson, Teresa Wu, N Gaw, H Yoon, A Hawkins-Daarud

Inventions, patent applications and/or license

  1. “Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization”, Fei Gao, Jay Shah, Yi Su, Teresa Wu, Application #: 63/285,002, Application Type: provisional, Country, USA, Filing Date: 12/01/2021
  2. “Deep learning based blob detection systems and methods” Yanzhe Xu, Teresa Wu, Fei Gao, Application #: US 63/164,699, Application Type: provisional, Country, USA, Filing Date: 03/23/2021
  3. “Patient-Specific Models for Glioblastoma (GBM) Heterogeneity and Extent Using Transfer Learning”, R. Swanson, L. Hu, J. Li, T. Wu, H. Yoon, Application #:  US2018/061887, Application Type:  Application (updated from provisional), Country:  USA, Filing Date:  11/19/2018
  4. “Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma”, K. R. Swanson, L. Hu, N. Tran, J. R. Mitchell, J. Li, Wu, File Date: 02/26/2018, Application #: US2019/019687, Application Type:  Application (updated from provisional), Country: USA, Filing Date: 2/26/2019

Tutorial (INFORMS)

  1. Wu, T., †Gaw, N., Xu, Y., Li, J., Wang, L., Fu, Y., Silva, A., Zwart, C., Borad, M., DeLeon, T., Patel, B., “Quantitative Imaging System for Cancer Diagnosis and Treatment Planning: An Interdisciplinary Approach“, available at https://doi.org/10.1287/educ.2017.0173, Tutorials In Operations Research, Oct. 2017.

  Book Chapters and Magazine 

  1. Wu, T., Blackhurst, J., Jalao, E. R., “Information Engineering”, The Handbook of Industrial and System Engineering, 2nd edition (edited by Adedeji Badiru), CRC Press, 2013.
  2. Ye, J., Wu, T., Li, J., Chen, K., “Machine Learning Approaches for the Neuroimaging Study of Alzheimer’s Disease”, IEEE Computer, pp. 99-101, April 2011.
  3. Wu, T., Blackhurst, J., “Modeling of Supply Chain Perturbations Caused by Information Errors”, Supply Chain Management and Knowledge Management – Integrating Critical Perspectives in Theory and Practice (edited by Ashish Dwivedi and Tim Butcher), published by Palgrave, 2008.
  4. *Feller, A., Wu, T., Shunk, D., “A Distributed Information System Architecture for Collaborative Product Development”, Integrated Intelligent Systems for Engineering Design (edited by Xuanfang Zha, R.J. Howlett), IOS Press,156-181, 2006.
  5. Wu, T., Blackhurst, J., “Information Engineering”, The Handbook of Industrial and System Engineering”, (edited by Adedeji Badiru), Taylor & Francis, 20.1-20.16, 2005
  6. Wu, T. and *Li, X. Y., “Data Storage and Management”, The Handbook of Data Mining (edited by N. Ye), 393-407, Lawrence Erlbaum Associates Publisher, 2003.

 Conference Papers/Presentations

  1. *Rahman Siddiquee, M.M., *Shah, J., Wu, T., Chong, C., Schwedt, T. and Li, B., 2022. HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease. In International Workshop on Simulation and Synthesis in Medical Imaging (pp. 43-54). Springer, Cham.
  2. †Pradhan, O., Hälleberg, D., Chen, Z., Wen, J., Wu, T., Candan, KS., O’Neill, Z., “Lagged-kNN Based Data Imputation Approach for Multi-Stream Building”, 5th Herrick Conferences, June 27, 2022.
  3. *Rahman Siddiquee, M., *Shah, J., Schwedt, T., Catherine Chong, Nikolova, S., Dumkrieger, G., Ross, K., Berisha, V., Li, J., Wu, T., “Classification of Post-Traumatic Headache (PTH) using Deep Learning on Structural Brain MRI data” American Headache Society 64th Annual Scientific Meeting, June 9–12, 2022, Denver, Colorado.
  4. *Rahman Siddiquee, M., *Shah, J., Schwedt, T., Catherine Chong, Nikolova, S., Dumkrieger, G., Ross, K., Berisha, V., Li, J., Wu, T., “Migraine Classification using Deep Learning on Structural Brain MRI data”, American Headache Society 64th Annual Scientific Meeting, June 9–12, 2022, Denver, Colorado.
  5. *Huang, J., *Li, T., *Xu, Y., Wu, T., Yoon, H., Charlton, J.R., Bennett, K.M., “EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning”, In. Proceedings of 2022 IISE Annual Conference, (Accepted).
  6. *Shah, J., Chen, K., Reiman, E., Li, B., Wu, T., Su, Y., “Transfer Learning based Deep Encoder Decoder Network for Amyloid PET Harmonization with Small Datasets”, Alzheimer’s Association International Conference, 2022
  7. *Huang, J., Yoon, H., Pradhan, O., Wu, T., Wen, J., O’Neill, Z., “Eigen-entropy: A Metric For Sampling Design”, INFORMS 2021 Annual Meeting, Anaheim, CA, USA, Oct 24-27, 2021.
  8. *Huang, J., Yoon, H., Pradhan, O., Wu, T., Wen, J., O’Neill, Z., “Eigen-entropy: A Metric For Sampling Design”, poster presented at: INFORMS 2021 Annual Meeting, Anaheim, CA, USA, Oct 24-27, 2021.
  9. *Huang, J., Wu, T., Yoon, H., Pradhan, O., Wen J., O’Neill, Z., “Automatic Fault Detection Baseline Construction for Building HVAC Systems using Joint Entropy and Enthalpy,” In. Proceedings of IIE Annual Conference, pp.536-541, 2021. (This paper is awarded Finalist in IISE Annual Conference DAIS Track Best Paper Award)
  10. *Huang, J., Yoon, H., Pradhan, O., Wu, T., Wen, J., O’Neill, Z., “Eigen-entropy: A Metric For Sampling Design”, INFORMS 2021 Annual Meeting, Anaheim, CA, USA, Oct 24-27, 2021.
  11. *Cai, F., *Patharkar, A., Chen, H., Wu, T, Lure, F., Chen, K., Chen, V., “The Application of Artificial Intelligence on Radar Gait Signature for Walking Abnormality Detection”, 50th Annual Applied Imagery Pattern Recognition Workshop (virtual), Oct. 12-Oct. 14th, 2021
  12. Chen, H., Lure, F., Wu, T., Geda, Y., Lockhart, T., Chen, K., Chen, V., “STRIDE – SysTematic Radar Intelligent analysis for Dementia risk Evaluation: A Feasibility Study Exploring Clinical Use of Gait Signature”, 50th Annual Applied Imagery Pattern Recognition Workshop (virtual), Oct. 12-Oct. 14th, 2021
  13. *Gao F, Li J, Wu, T, Chen K, Liu X, Baxter L, Caselli R. Bi-threshold Frequent Subgraph Mining for Alzheimer Disease Risk Assessment. SPIE medical imaging conference, Feb 2018.
  14. *Liu X, Chen K, Wu, T, Weidman D, Lure F, Li J, ADMultiImg: A Novel Missing Modality Transfer Learning based CAD System for Diagnosis of MCI due to AD using Incomplete Multi-Modality Imaging Data. SPIE medical imaging conference, Feb 2018
  15. *Gao F, Li J, Wu, T, Chen K, Lure F, Weidman D. Diagnosis on Mild Cognitive Impairment Patients for Alzheimer Disease with Missing Data. In Healthcare Informatics (ICHI), 2017 IEEE International Conference on 2017 Aug 23 (pp. 547-551). IEEE.
  16. *Gao, F., Xu, Y., Panda, A., Zhang, M., Hanson, J., Su, C., Wu, T., Pavlicek, W., James, J., “MR Efficiency using automated MRI-desktop eProtocol”, Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380Z (March 13, 2017); doi:10.1117/12.2249712
  17. *Gao, F., Zhang, M., Wu, T, Bennett, K., “3D Small structure detection in medical image using texture analysis”, 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society, DOI:10.1109/EMBC.2016.7592201
  18. Zhang, M., * Fu, Y., Bennett, K., Wu, T., “Computational Efficient Variational Bayesian Gaussian Mixture Models via Coreset, International Conference on Computer, Information and Telecommunication Systems, 2016, DOI:1109/CITS.2016.7546405
  19. *Zhang, M, Pavlicek, WM, Panda, A., Langer, S., Morin, R., Fetterly, K., Paden, R., Hanson, J., Wu, T., “DICOM index tracker II: advanced system for enterprise-wide quality assurance and patient safety monitoring”, SPIE Medical Imaging 2015.
  20. *Zhang, M., Wu, T., Bennett, K., “A Novel Hessian Based Algorithm for Kidney Glomeruli Detection in 3D MRI”, Processing of SPIE 9413, Medical Imaging, 2015: Image Processing, 94132N (March 20, 2015), doi:10.1117/12.2081484
  21. Odonkor, P., Lewis, K., Wen J., and Wu, T., 2014, “Energy Optimization in Net-Zero Energy Building Clusters,” ASME International Design Technical Conferences, Design Automation Conference, Buffalo, NY, DETC2014-34970. (awarded top 6 papers for the Design Automation Conference)
  22. *Cui, C., Wu, T, Hu, M., Weir, J.D., Chu, X., “Accuracy vs. Robustness: Bi-Criteria Optimized Ensemble of Metamodels”, Winter Simulation Conference, 2014
  23. *Jalao, E., *Worger, D., Wu, T., Wirthlin, J., Colombi, J., “Effect of the Analysis of Alternatives on the DoD Acquisition System” Proceedings of the 2013 Industrial and Systems Engineering Research Conference. May 18-22, 2013, Puerto Rico, USA.
  24. *Worger, D., *Jalao, E. Auger, C., Baldus, L., Yoshimoto, B., Wirthlin, R., Colombi, J.,  Wu, T. “Bottleneck Analysis on the DoD Pre-Milestone B Acquisition Process” Proceedings of 10th Annual Acquisition Research Symposium. Monterey CA, 2013.
  25. *Jalao, E.,  Shunk, D., Wu, T., “Prioritization of Applications for Software-as-a-Service Migration Using Total Life Cycle Costs and the Analytic Network Process” Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering 2012, WCE 2012, 4-6 July, 2012, London, U.K., pp1352-1357. (received best paper certificate of merit award)
  26. *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. Proceedings of the 25th Annual Conference on Neural Information Processing Systems (NIPS), December 13-15, 2011, Granada, Spain (acceptance rate 4.8%)
  27. *Hu, M., Wen, J., *Li, F., *Haghnevis, M., *Khodadadegan, Y., *Sanchez, L.M., *Wang, S., *Zhuang, X., Wu, T., “An Agent based Simulation for Building Energy System Modeling”, Proceeding of 2010 ASME Dynamic Systems and Control Conference,  Cambridge, Massachusetts, September, 2010.
  28. *Ramirez, A., Fowler, J.W. and Wu, T., “Bi-Criteria Analysis of Ambulance Diversion Policies”. In Proceedings of the 2010 Winter Simulation Conference, ed. B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yucesan. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc. (received best student paper award).
  29. *Ramirez, A., Fowler, J.W., Gel, E.S. and Wu, T., “Analysis of Min-Max Ambulance Diversion Policies Using Queuing Theory”. In Proceedings of the 2010 Industrial Engineering Research Conference.
  30. *Ramirez, A., Fowler, J.W. and T., “Analysis of Ambulance Diversion Policies for a Large-Size Hospital”. In Proceedings of the 2009 Winter Simulation Conference, ed. M.D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R.G. Ingalls. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
  31. *Ramirez A., Fowler J., Wu T., “Modeling of Regional Healthcare Delivery Networks using Distributed Simulation”. In Proceedings of the 2009 Industrial Engineering Research Conference.
  32. *Sun, L., *Patel, R., *Liu, J., Chen, K., Wu, T., Li, J., Reiman, E., Ye, J., “Mining Brain Region Connectivity for Alzheimer’s Disease Study via Sparse Inverse Covariance Estimation”, KDD 2009 (acceptance rate 14%).
  33. Ye, J., Chen, K., Wu, T., Li, J., *Zhao, Z., *Patel, R., *Bae, M., Janardan, R., Liu, H., Alexander, G., Reimean E., “Heterogeneous Data Fusion for Alzheimer’s Disease Study”, KDD 2008 (acceptance rate 14%).
  34. *Li, F., Wu, T., “An Importance Sampling Based Approach for Reliability Analysis”, IEEE CASE 2007, Sep. 22-25, 2007, Scottsdale, Arizona.
  35. *Chen, Y., Fowler, J., Wu, T., Ambrose, E., Hargaden, V. “An Adaptive Distributed Simulation Framework for a Server Fulfillment Supply Chain”. IEEE Conference on Automation Science and Engineering, pp. 649-655, Oct. 8-10, 2006.
  36. Wu, T., Fowler, J., Callerman, T. and *Moorehead, A.,“Multi-stage DEA as a Measurement of Progress in Environmentally Benign Manufacturing”, The 16th International Conference on Flexible Automation and Intelligent Manufacturing, pp. 221-228, Limerick, Ireland, June, 2006.
  37. *Parmar, D., Wu, T., Fowler, J., Callarman, T. and Hargaden, V. “An intergrated framework for responsive supply chain management,” The 16th International Conference on Flexible Automation and Intelligent Manufacturing, pp. 859-866, Limerick, Ireland, June, 2006.
  38. *Ganguly, S., Wu, T., “A Principle-Agent Model For Distributed, Collaborative Design Negotiation”, Proceedings of Integrated Design and Process Technology, IDPT-2005, pp. 890-896, June 12-17, 2005, Beijing, China.
  39. *Xie, N., Wu, T., “VE4PD: a distributed information system for collaborative product development”, Proceedings of SPIE on Intelligent Systems in Design and Manufacturing V, 25-35, Oct, 25-26, 2004, Philadelphia, USA
  40. Wu, T., Blackhurst, J., “Modeling methodology for supply chain synthesis and disruption analysis”, Proceedings of SPIE on Intelligent Systems in Design and Manufacturing V, 36-46, Oct, 25-26, 2004, Philadelphia, USA
  41. Tseng, T-L., M.C. Jothishankar, Wu, T., Xing, G.M., Jiang, F.H., “Applying Data Mining Approaches for Defect Diagnosis in Manufacturing Industry”, Proceedings of Industrial Engineering Research 2004 Conference, Houston, Texas, May 15-19, 2004.
  42. Wu, T., *Appalla, R., “An Effective Supplier Assessment Model”, Proceedings of the 8th Annual International Conference on Industrial Engineering – Theory, Applications and Practice, Las Vegas, Nevada, USA, November 10-12, 2003, pp. 915-920.
  43. *Shiau, J-Y., Wu, T., Wolfe, P., “Data Quality in Data Conversion”, Asia Pacific Industrial Engineering and Management Systems Conference, Taipei, Taiwan, Dec., 2002.
  44. Blackhurst, J. and Wu, T., “Research Issues in Supply Chain Management”, the 6th World Multiconference on Systematics, Cybernetics and Informatics, 491-496, July, 2002, Orlando, Florida.
  45. Dong, J., Shunk, D., Wu, T., “Agent Based Enterprise Information System and its E-Marketplaces”, the 6th world Multiconference on Systematics, Cybernetics and Informatics, 497-502, July, 2002, Orlando, Florida.
  46. Schnell, T., Wu, T., (2000), “Applying Eye Tracking As Alternative Approach for Activation of Controls and Functions in Aircraft”, Proceedings of the5th International Conference On Human Interaction with Complex Systems (HICS), April, 30 – May, 2, 2000, Urbana, Illinois, USA, pp 113.