教育经历
2004—2008，清华大学，材料科学与工程和经济学，学士
2008
—2012，新加坡国立大学，工业系统工程与管理，博士
工作经历
2014/02—2020/06，新加坡国立大学，工业系统工程与管理，助理教授
2020/07—至今，新加坡国立大学，工业系统工程与管理，副教授
研究领域
数据驱动的运营管理：从数据到决策
数理统计与工业统计：半参数模型、假设检验、工业数据分析、数据收集方案设计
系统弹性：可靠性工程、视情维修、复杂系统建模
荣誉奖项
NUS CDE Young Researcher Award, 2024.
NUS CDE College Teaching Excellence Award, 2022/2023.
Best Paper Award for the paper Robust Condition-Based Production and Maintenance Planning for
Degradation Management", INFORMS Conference on Quality, Statistics, and Reliability (ICQSR) 2023,
NC, US.
Best Application Paper Finalist for the paper Robust Condition-Based Production and Maintenance
Planning for Degradation Management, Quality, Statistics, and Reliability Section of INFORMS Annual
Conference 2022, Indiana, US.
Dean's Chair, 01/2022-now.
代表性论文
[1] Li, X., Ye Z.S., and Zhao, X. (2025) Sieve estimation of the accelerated mean model based on panel count data. Journal of Statistical Planning and Inference, 237: 106247.
[2] Lu, B., Wang, X., Cui, W., and Ye Z.S. (2025) A predictive opportunistic maintenance policy for a serial parallel multi-station manufacturing system with heterogeneous components. Reliability Engineering & System Safety, 256, 110711.
[3] Wang, Y., Hu, X., Tong, J., Ye Z.S., Chen, Y. and Tang, C.Y. (2025) Semiparametric Sieve Estimation for Survival Data with Two-layer Censoring, Biometrika, asaf006.
[4] Ma, L., Jiang, B., Lu, N., Guo, Q., and Ye Z.S.. (2024) Aeroengine Bearing Time-Varying Skidding Assessment With Prior Knowledge-Embedded Dual Feedback Spatial-Temporal GCN. IEEE Transactions on Cybernetics, Early Access.
[5] Xiao, P., Wang, Y., Ye Z.S. and Liu, W. (2024) Remaining Useful Life Prediction Based on Forward Intensity, Technometrics, 1-16.
[6] Zhang, X., Ye Z.S., and Haskell, W.B. (2024) Error propagation in asymptotic analysis of the data-driven (s, S) inventory policy. Operations Research, 73(1), 1-21.
[7] Xiao, X, Ye Z.S., Revie M. (2024) Learning Local Cascading Failure Pattern from Massive Network Failure Data, Journal of the Royal Statistical Society, Series C, 73(5), 1155-1184.
[8] Zhai, Q., Ye Z.S., Li, C., Revie, M., and Dunson, D. (2024) Modeling recurrent failures on large directed networks. Journal of the American Statistical Association, 1-15.
[9] Wang, J., Ye Z.S., and Chen, Y. (2024) Likelihood-based Inference under Non-Convex Boundary Constraints Biometrika, 111(2), 591-607.
[10] Wen, P., Ye Z.S., Li, Y., Chen, S., Xie, P., and Zhao, S. (2024). Physics-informed neural networks for prognostics and health management of lithium-ion batteries. IEEE Transactions on Intelligent Vehicles, 9(1), 2276-2289.
NUS网站
https://cde.nus.edu.sg/isem/staff/ye-zhisheng/
