助理教授
教育经历：
博士，工业与系统工程，美国罗格斯大学，2020
硕士，工业与系统工程，美国罗格斯大学，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：
王常玺博士在罗格斯大学获得博士学位后，于2021年加入四川大学匹兹堡学院。他的研究兴趣包括物联网、机器学习、可靠性工程、NDT&E、ALT和ADT。除学术经验外，王常玺博士亦拥有在高露洁公司担任数据科学家的行业经验。
研究方向：
物联网，机器学习，可靠性工程，随机过程理论，生存数据分析，无损检测与评价，加速寿命/老化试验
工作经历：
助理教授，四川大学匹兹堡学院，2021.2-今
数据科学家，高露洁技术中心，新泽西，2020.2-2021.1
科研项目：
1. 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
2. 四川省人才计划项目，50万元，2024.4-2026.3，负责人
3. 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
4. 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
5. 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
6. 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，项目骨干
7. 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
奖励和荣誉：
1. 四川省人才计划入选者, 2024
2. 四川大学本科优秀毕业论文二等奖, 2022
3. 四川省海外高层次留学人才, 2021
4. Best Paper, IISE Transactions – Data Science, Quality and Reliability 2021
5. 2nd Place, Data Analytics Competition, Data Analytics and Information Systems Division, Institute of Industrial and Systems Engineers, 2020
6. Winner, Data Challenge Competition, Quality Control & Reliability Engineering Division, Institute of Industrial and Systems Engineers, 2019
7. Finalist, Best Paper Competition, Quality Control & Reliability Engineering Division, Institute of Industrial and Systems Engineers, 2019
8. Finalist, Best Paper Competition, New Jersey Chapter, Institute for Operations Research and the Management Science,2017
期刊论文代表作:
2. 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, 149(C), 1028707.
3. 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, 24(1), 1184.
4. T. Wu, C. Wang, K. 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.
5. C. Wang, T. Wu, T. Wang, K. Li. Missing Data Interpolation and Multi-Sensors Integration and Its Application in Accelerated Degradation Data. Quality and Reliability Engineering International, 2023, 40(1), 181-201.
6. C. Wang, Q. Liu, H. Zhou, T. Wu, H. Liu, J. Huang, Y. Zhuo, Z. Li, K. Li. Anomaly Prediction of CT Equipment Based on IoMT Data. BMC Medical Informatics and Decision Making, 2023, 166:1-14.
7. C. Wang and E. A. Elsayed. Stochastic Modeling of Degradation Branching Processes. IISE Transactions, 2020, 53(3): 1-10.
8. C. Wang and E. A. Elsayed. Stochastic Modeling of Corrosion Growth. Reliability Engineering & System Safety, 2020, 204: 0-107120
9. 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.
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. Y. Tang, X. Chen, J. Zhao, and C. Wang. “Reliability Estimation of Complex Systems Based on the Internet of Things,” presented at the 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes. Yibin, China, September, 2023.
2. C. Wang. “Stochastic Modeling of Degradation Branching Processes,” presented at the 2023 16th Operations Research Society of China Annual Conference. Changsha, Hunan, 2023.
3. H. Zhou, Q. Liu, J. Huang, Z. Li, and C. Wang. “Reliability Estimation of Medical Equipment Based on Big Data,” presented at the 2022 8th International Symposium on System Security, Safety, and Reliability. Chongqing, China, October, 2022.
4. 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.
5. 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, and K. Li. “An Exploration for New Strategy: Degradation Modeling and Treatment Scheduling for the Degenerative Spine Based on Predictive Models,” presented at the 2022 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD). IEEE, 2022: 1-6.
6. C. Wang and E. A. Elsayed, “Stochastic Modeling of Branching Degradation,” 2019 IISE Annual Conference. Orlando, Florida, May, 2019
7. S. Guo, C. Wang, “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.
专利：
1. 一种基于工业物联网数据的故障预测方法和系统，2022116071406
2. 一种基于物联网数据的CT设备异常预测方法，202211619820X
3. 一种基于物联网数据的MRI设备异常检测方法和系统，2023101030183
学术任职：
国际运筹与管理学会，会员及会议主席，2017年至今
国际工业与系统工程师学会，会员，2018年至今
担任以下期刊审稿人：
IISE Transactions
Reliability Engineering and System Safety
IEEE Transactions on Automation Science and Engineering
Quality and Reliability Engineering International
Applied Stochastic Models in Business and Industry
担任以下期刊编辑委员会委员：
International Journal of Applied Management Science