Changxi Wang

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Assistant Professor


Ph.D., Industrial and Systems Engineering, Rutgers University, 2020

M.S., Industrial and Systems Engineering, Rutgers University, 2020

M.E., Materials Processing Engineering, Harbin Institute of Technology, 2015

B.E., Materials Science Engineering Honors Class, Harbin Institute of Technology, 2013



Dr. Changxi Wang joined SCUPI in 2021 after receiving his Ph.D. from Rutgers University. His current research interests include IoT, Machine Learning, Reliability Engineering, NDT&E, ALT and ADT. In addition to his academic experience, he also has industry experience as a Data Scientist at Colgate.


IoT, Machine Learning, Reliability Engineering, Stochastic Models, Life Data Analysis, Nondestructive Testing and Evaluation, Accelerated Life/Degradation Testing


Assistant Professor, Sichuan University – Pittsburgh Institute, 2021.2-Present

Data Scientist, Colgate Technology Center, NJ, 2020.2-2021.1


1. Reliability Modeling Based on Generalized Degradation Branching Stochastic Processes, National Natural Science Foundation of China, 12201441, ¥300,000,2023.1-2025.12, PI

2. Intelligent Reliability Estimation and Operation Management of Medical Equipment Based on Big Data of Internet-of-Things, Natural Science Foundation of Sichuan, 23NSFSC3794, ¥100,000, 2023.1-2024.12, PI

3. Internet-of-Things-Big-Data-Driven Anomaly Detection and Medical Resources Allocation Model of Large-scale Medical Equipment, Med-X for Informatics, Sichuan University, YGJC006, ¥700,000, 2022.6-2023.6, PI

4. The Research and Evaluation System Construction of Common Key Technologies of Reliability of Range of Emergency Treatment Equipment, National Key Research and Development Program of China, Ministry of Science and Technology of the People’s Republic of China, Major Participant

5. Master-Slave Somatosensory Control Nursing Robot for New Crown Medical Care Scenarios, Sichuan International Science and Technology Innovation Cooperation/Hong Kong, Macao and Taiwan Science and Technology Innovation Cooperation Project, 22GJHZ0184, 2022.1-2023.12, Participant


1. Sichuan Overseas High-Level Talent, 2021

2. Best Paper, IISE Transactions – Data Science, Quality and Reliability, 2021

3. 2nd Place, Data Analytics Competition, Data Analytics and Information Systems Division, Institute of Industrial and Systems Engineers, 2020

4. Winner, Data Challenge Competition, Quality Control & Reliability Engineering Division, Institute of Industrial and Systems Engineers, 2019

5. Finalist, Best Paper Competition, Quality Control & Reliability Engineering Division, Institute of Industrial and Systems Engineers, 2019

6. Finalist, Best Paper Competition, New Jersey Chapter, Institute for Operations Research and the Management Science, 2017


1. C. Wang and E. A. Elsayed. Stochastic Modeling of Degradation Branching Processes. IISE Transactions, 2020, 53(3): 1-10

2. C. Wang and E. A. Elsayed. Stochastic Modeling of Corrosion Growth. Reliability Engineering & System Safety, 2020, 204: 0-107120

3. 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

4. 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

5. 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


1. 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

2. 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

3. 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

4. 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


1. C. Wang and E. A. Elsayed, “Stochastic Modeling of Branching Degradation,” 2019 IISE Annual Conference. Orlando, Florida, May, 2019

2. 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

3. C. Wang and E. A. Elsayed,“Stochastic Modeling of Corrosion Growth,”presented at 2018 INFORMS Annual Meeting”. Phoenix, Arizona, November, 2018

4. C. Wang and E. A. Elsayed, “Missing Degradation Data Interpolation,” presented at the workshop “Data and Decisions”. Arizona, Phoenix, November, 2018

5. 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


An Anomaly Prediction Method of CT Equipment Based on IoMT Data

A Fault Prediction Method Based on Industrial Internet of Things Data


Institute for Operations Research and the Management Sciences (INFORMS), Member and Session Chair (2017-Present).

Institute of Industrial and Systems Engineers (IISE), Member (2018-Present).

Reviewer of the Following Journals:

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

Editorial Board Member:

International Journal of Applied Management Science