Nan Chen

Associate Professor in the Department of Industrial Systems Engineering and Management at National University of Singapore

Schools

  • National University of Singapore

Links

Biography

National University of Singapore

Research Interests

  • System informatics and prognostic health management
  • Performance modeling & surveillance of service systems
  • Statistical quality & process control
  • Applied Statistics

Companies

  • Assistant Professor National University of Singapore (2010)
  • Research Assistant University of Wisconsin-Madison (2006 — 2010)
  • Research Intern Microsoft Research Asia (2006 — 2006)

Education

  • Master of Science University of Wisconsin-Madison (2008 — 2010)
  • Ph.D. University of Wisconsin-Madison (2006 — 2010)
  • Master of Science University of Wisconsin-Madison (2008 — 2009)
  • BS Tsinghua University (2002 — 2006)

Selected Publications

  • Dong, H., Chen, N., and Wang, K. (2018) Modeling and Change Detection for Count-weighted Multi-layer Networks, under review.
  • Zhu, J., and Chen, N.* (2018) Estimation of Bearing Remaining Useful Life based on Multiscale Convolutional Neural Network, under 2nd review.
  • Peng, W., Ye, Z., and Chen, N. (2018) Joint RUL Prediction of Multiple Systems with Multivariate Degradation, under revision.
  • Wang, W., Chen, N., Chen, X., and Yang, L. (2018) A Variational Inference-based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling, submitted.
  • Zhang, C., and Chen, N.* (2018) Statistical Analysis of Stochastic Simulation Output from Parallel Computing, ACM Transactions on Modeling and Computer Simulation, accepted.
  • Ren, H., Chen, N.*, and Wang, Z. (2018) Phase-II Monitoring in Multichannel Profile Observations, Journal of Quality Technology, accepted.
  • Sun, Q., Ye, Z., and Chen, N. (2018) Optimal Inspection and Replacement Policies for MultiUnit Systems Subject to Degradation, IEEE Transactions on Reliability, 67(1), 401 – 413.
  • Dong, H., Chen, N., and Wang, K. (2017+) Wafer Yield Prediction using Derived Spatial Variables, Quality and Reliability Engineering International, accepted.

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