副教授
教育经历：
博士，工业与系统工程，罗格斯大学，2020
硕士，工业与系统工程，罗格斯大学，2020
硕士，材料加工工程，哈尔滨工业大学，2015
学士，材料学英才班，哈尔滨工业大学，2013
邮箱：
简介：
四川省海外高层次引进人才，入选四川省委人才计划项目，教育部本科教育教学评估专家组成员，美国罗格斯大学博士，可靠性与智能风险管理实验室负责人，从事医疗健康领域的可靠性研究。主持多项国家自然科学基金、国家重点研发计划、四川省科技计划、四川大学“医学+信息”项目、华西医院“1.3.5工程”人工智能项目等课题及子课题，到账金额200余万元。在IISE Trans、RESS等期刊上发表论文20余篇。获2024年全国工业工程博士生优秀论文奖（指导教师）、2021年美国工业与系统工程师学会（IISE）会刊IISE Trans最佳论文、2019/2020年IISE数据大赛第一/二名、2017/2019年国际运筹与管理科学学会（INFORMS NJ）/IISE最佳学生论文提名奖等奖项。
研究方向：
物联网，智能运维，可靠性工程，随机过程理论，医学信息学
工作经历：
副教授，四川大学匹兹堡学院，2025.9-今
助理教授，四川大学匹兹堡学院，2021.2-2025.9
数据科学家，高露洁技术中心，新泽西，2020.2-2021.1
科研项目：
1. 面向复杂任务场景的智能无人系统可靠性管理，国家自然科学基金 重点国际（地区）合作研究项目，项目号W2511076，25万元，2026.1.1-2030.12.31，子课题负责人（与清华大学工业工程系联合申报）
2. 高性能3D打印聚醚酮酮仿生骨植入器械开发，国家重点研发计划，中华人民共和国科学技术部，项目号2025YFC2424900，20万元，2025.11-2027.10，子课题负责人
3. 四川省人才计划项目，中共四川省委人才工作领导小组，50万元，2024.4-2027.3，负责人
4. 基于物联网的医学装备智能故障管理及可靠性评估，四川大学华西医院“1.3.5工程”人工智能项目，项目号ZYAI24031，20万元，2024.9-2025.8，工科负责人，已结题
5. 基于广义分支退化随机过程的系统可靠性模型及其应用，国家自然科学基金，项目号12201441，30万元，2023.1-2025.12，负责人，已结题
6 基于物联网大数据的医学仪器可靠性智能评估及运营管理体系研究，四川省自然科学基金，四川省科学技术厅，项目号23NSFSC3794，10万元，2023.1-2024.12，负责人，已结题
7. 物联网大数据驱动的大型医学设备异常预测模型及资源配置研究，“医学+信息”交叉学科建设开放项目，四川大学“医学+信息”中心，项目号YGJC006，70万元，2022.6-2023.6，工科负责人，已结题
8. 应急救治系列装备可靠性共性关键技术研究和评价体系构建，国家重点研发计划，中华人民共和国科学技术部，2022YFC2407601，2022.11-2025.10，子课题负责人，已结题
9. 面向新冠医护场景的主从体感控制护理机器人，四川省国际科技创新合作/港澳台科技创新合作项目，四川省科学技术厅，项目号22GJHZ0184，2022.1-2023.12，参研，已结题
奖励和荣誉：
1. 清华大学全国工业工程博士生学术论坛最佳会议论文奖 (指导教师)，2024
2. 四川省人才计划入选者, 中共四川省委人才工作领导小组，2024
3. 四川大学本科优秀毕业论文二等奖, 2022
4. 四川省海外高层次留学人才, 2021
5. Best Paper, IISE Transactions – Data Science, Quality and Reliability 2021
6. 2nd Place, Data Analytics Competition, Data Analytics and Information Systems Division, Institute of Industrial and Systems Engineers, 2020
7. Winner, Data Challenge Competition, Quality Control & Reliability Engineering Division, Institute of Industrial and Systems Engineers, 2019
8. Finalist, Best Paper Competition, Quality Control & Reliability Engineering Division, Institute of Industrial and Systems Engineers, 2019
9. Finalist, Best Paper Competition, New Jersey Chapter, Institute for Operations Research and the Management Science, 2017
社会及学术任职：
1. 教育部本科教育教学评估专家组成员，2025年
2. 四川大学华西医院医学人工智能微专业授课专家，2025年
3. 华西麻醉信息与智能医学培训班授课专家，2025年
4. 中国运筹学会（ORSC）会员，2023年至今
5. 国际运筹与管理学会（INFORMS）会员及分会主席，2017年至今
6. 国际工业与系统工程师学会（IISE）会员，2018年至今
期刊论文代表作:
1. J. Yang, T. Lin, H. Li, T. Wu, K. Li#, C. Wang#. Towards AI-Driven Smart Maintenance of Computed Tomography Equipment: From Health Indicator Design to Transferable Remaining Useful Life Prediction, Reliability Engineering & System Safety, 2026, In press
2. T. Wu, T. Wang, H. Li, C. Wang# and K. Li. Stochastic Modeling of Crack Branching under Uncertainties: A Degradation Branching Framework, Reliability Engineering & System Safety, 2026, In press
3. T. Wu, L. Ma, Y. Cheng, K. Zhang, K. Li, Y. Yang, H. Liu and C. Wang# Stochastic Modeling of Human Lumbar Functional Spinal Units System Degeneration. IISE Transactions, 2026, 58(2), 162–180.
4. Y. Tang, Y. Zhou, T. Wu, C. Wang#, Z. Li, K. Li. AI-driven predictive maintenance for medical imaging equipment: a deep learning framework based on the IoMT data, Reliability Engineering & System Safety, 2026, 270, 112152.
5. T. Wu, C. Ma, C. Wang#, K. Li. Stochastic Modeling of Inter-Dependent System Degradation Branching Processes: Applications to Human Cervical Spine Degeneration, IISE Transactions on Healthcare Systems Engineering, 2025: 1-14
6. H. Zhou, Z. Li, T. Wu, C. Wang# and K. Li#. Prognostic and Health Management of CT Equipment Via a Distance Self-Attention Network Using Internet of Things. IEEE Internet of Things Journal, 2024, 11(19): 31338-31354
7. H. Zhou, Q. Liu, H. Liu, Z. Chen, Z. Li, Y. Zhuo, K. Li, C. Wang#, J. Huang#. Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment. Artificial Intelligence in Medicine, 2024, 102807.
8. T. Wang, H. Liu, X. Zhou, C. Wang#. The effect of retirement on physical and mental health in China: a nonparametric fuzzy regression discontinuity study. BMC public health. 2024 Apr 27;24(1):1184.
9. C. Wang, T. Wu, T. Wang and K. Li#, Missing data interpolation and multi-sensors integration and its application in accelerated degradation data, Quality and Reliability Engineering International, 2023, 1, 1-21
10. C. Wang, Q. Liu, H. Zhou, T. Wu, H. Liu, J. Huang#, Y. Zhuo, Z. Li and K. Li#, Anomaly prediction of CT equipment based on IoMT data, BMC Medical Informatics and Decision Making, 2023, 166: 1-14
11. C. Wang and E. A. Elsayed#. Stochastic Modeling of Degradation Branching Processes. IISE Transactions, 2020, 53(3): 1-10
12. C. Wang and E. A. Elsayed#. Stochastic Modeling of Corrosion Growth. Reliability Engineering & System Safety, 2020, 204: 0-107120
13. J. Guo, C. Wang, J. Cabrera and E. A. Elsayed#. Improved inverse Gaussian process and bootstrap: Degradation and reliability metrics. Reliability Engineering & System Safety, 2018, 178, 269-277.
14. T. Wu, C. Wang# & Kang. Li#. Quantitative analysis and stochastic modeling of osteophyte formation and growth process on human vertebrae based on radiographs: a follow-up study. Scientific Reports, 2024, 14, 9393
15. T. Wang, H. Liu, X. Zhou and C. Wang#. Trends in prevalence of hypertension and high-normal blood pressure among US adults, 1999–2018, Scientific Reports, 2024, 14 (1), 25503
16. B. Liu, T. Gang#, C. Wan, C. Wang and Z. Luo. Analysis of nonlinear modulation between sound and vibrations in metallic structure and its use for damage detection. Nondestructive Testing and Evaluation, 2015, 30(3), 277-290.
17. C. Wan, T. Gang#, B. Liu and C. Wang. Characterization of the fatigue process of U71Mn steel based on non-linear ultrasonic technology. Insight-Non-Destructive Testing and Condition Monitoring, 2015, 57(7), 389-394.
经同行审稿的会议论文：
1. Y. Tang, X. Chen, J. Zhao, Q. Liu, H. Zhou, Z. Chen, Z. Li, Y. Zhuo, K. Li, C. Wang, J. Huang. Reliability Estimation of Complex Systems Based on the Internet of Things. The 13th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (CAA SAFEPROCESS 2023), Yibin, China, 2023.
2. T. Wu, Y. Tang, H. Liu, Y. Yang, K. Zhang, L. Ma, H. Jiang, X. Wu, Z. Bai, J. Wen, F. Li, Y. Xia, C. Wang, K. Li. An Exploration for New Strategy: Degradation Modeling and Treatment Scheduling for the Degenerative Spine based on Predictive Models. International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), Harbin, 2022
3. H. Zhou, T. Wu, Q. Liu, Y. Zhuo, J. Huang, Z. Li, C. Wang and K. Li. Reliability Estimation of Medical Equipment Based on Big Data, The 8th IEEE International Symposium on System Security, Safety, and Reliability (ISSSR), Chongqing, 2022
4. T. Wu, C. Wang, K. Zhang, K. Li, Y. Cheng, Reliability Estimation and Risk Assessment of the Human Spine Based on Wiener Process, The 12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), Chengdu, 2022
5. C. Wang, E. A. Elsayed, K. Li. and J. Cabrera. “Multisensor Degradation Data Fusion and Remaining Life Prediction,” ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, V005T10A003-V005T10A003, Hawaii, 2017.
学术会议报告：
1. C. Wang, Time-Series Data-Driven Decision-Making in Healthcare Systems, The Greater Bay Area Artificial Intelligence and Data Science Application Summit & the 18th China‑R Conference, Guangzhou, Guangdong, China, July, 2025 (invited talk)
2. T. Wu, K. Li and C. Wang, Reliability Modeling of Human Lumbar Spine Degeneration, 2024 National Industrial Engineering Doctoral Academic Forum, Department of Industrial Engineering, Tsinghua University, Beijing, China, December, 2024
3. C. Wang, Reliability Engineering in Healthcare Systems, 2024 National Industrial Engineering Annual Conference, Zhuhai, China
4. C. Wang. “Stochastic Modeling of Degradation Branching Processes,” presented at the 2023 16th Operations Research Society of China Annual Conference. Changsha, Hunan, 2023.
5. C. Wang, H. Zhou, T. Wu, J. Huang, K. Li, Z. Li, and Q. Liu. “Anomaly Detection of CT Equipment Based on IoT Data,” presented at the 2022 International Conference for Chinese Scholars in Industrial Engineering. Chengdu, China, April, 2022.
6. C. Wang and E. A. Elsayed, “Stochastic Modeling of Branching Degradation,” 2019 IISE Annual Conference. Orlando, Florida, May, 2019
7. C. Wang, S. Guo, “Paper Break Prediction Based on Classification in Multivariate Time Series,” presented at the 2019 IISE Annual Conference. Orlando, Florida, May, 2019
8. C. Wang and E. A. Elsayed, “Stochastic Modeling of Corrosion Growth,” presented at “2018 INFORMS Annual Meeting”. Phoenix, Arizona, November, 2018.
9. C. Wang and E. A. Elsayed, “Missing Degradation Data Interpolation,” presented at the workshop “Data and Decisions”. Arizona, Phoenix, November, 2018
10. C. Wang and E. A. Elsayed, “Gamma Process Based Corrosion Volume Loss Stochastic Modeling and Reliability Analysis,” presented at 2017 INFORMS New Jersey Chapter Student Contest. Piscataway, New Jersey, October, 2017.
学术会议海报：
1. C. Wang and E. A. Elsayed, “Stochastic Modeling of Branching Degradation,” 2019 IISE Annual Conference QCRE Best Paper Competition. Orlando, Florida, May, 2019
2. C. Wang and E. A. Elsayed, Stochastic Modeling of Corrosion Growth, 2018 INFORMS Annual Meeting”. Phoenix, Arizona, November, 2018
专利：
1. 一种基于工业物联网数据的故障预测方法和系统，ZL202211619820.X，已授权
2. 一种基于物联网数据的CT设备异常预测方法，202211619820X，待授权
3. 一种基于物联网数据的MRI设备异常检测方法和系统，2023101030183，待授权
担任以下期刊审稿人：
IISE Transactions
IISE Transactions on Healthcare Systems Engineering
Reliability Engineering and System Safety
IEEE Internet of Things Journal
IEEE Transactions on Reliability
IEEE Transactions on Automation Science and Engineering
Quality and Reliability Engineering International
INFORMS Journal on Data Science
开设课程：
IE1082: Probabilistic Methods in Operations Research
IE1040: Engineering Economic Analysis
IE1083: Simulation Modeling
Technical Elective: Data Analytics in IE
Technical Elective: Reliability Engineering
Technical Elective: Data Mining