Wei Xie

Assistant Professor, Industrial and Systems Engineering

Wei Xie joined the ISE department as an Assistant Professor in August 2014. She received her Ph.D. in Industrial Engineering and Management Sciences at Northwestern University in 2014. Her research interests focus on computer simulation, data analytics, risk and reliability management with applications, including supply chains, semiconductor and biopharma manufacturing, and smart power grids.

Education

Ph.D. and M.S. Industrial Engineering and Management Sciences (Northwestern University), M.S. Engineering Mechanics (University of Nebraska-Lincoln), B.S. Mechanical Engineering (Yangtze University).

Research Focus
  • Stochastic Simulation
  • Risk Management
  • Data Analytics
  • Simulation Optimization
Select Works
  • Yi, Y., W. Xie (2017). An Efficient Budget Allocation Approach for Quantifying the Impact of Input Uncertainty in Stochastic Simulation, accepted by ACM Transactions on Modeling and Computer Simulation.
  • Xie, W., C. Li, P. Zhang (2017). A Bayesian Nonparametric Hierarchical Framework for Uncertainty Quantification in Simulation, submitted.
  • Xie, W., C. Li, P. Zhang (2017). A Factor-Based Bayesian Framework for Risk Analysis in Large-Scale Stochastic Simulation, submitted.
  • Xie, W., P. Zhang, Q. Zhang (2017). A Stochastic Simulation Calibration for Real-Time System Control. Proceedings of the 2017 Winter Simulation Conference.
  • Zhang, Q., W. Xie (2017). Asymmetric Kriging Emulator for Stochastic Simulation. Proceedings of the 2017 Winter Simulation Conference.
  • Wang, B., Q. Zhang, W. Xie (2017). Bayesian Sequential Calibration Using Sample Paths. Proceedings of the 2017 Winter Simulation Conference.
  • Xie, W. B. L. Nelson, R. R. Barton (2016). Multivariate Input Uncertainty in Output Analysis for Stochastic Simulation, ACM Transactions on Modeling and Computer Simulation, Vol. 27, Issue 1, No. 5.
  • Bostanabad, R., A. T. Bui, W. Xie, D. W. Apley, W. Chen (2016). Stochastic Microstructure Characterization and Reconstruction via Supervised Learning. Acta Materialia. Vol. 103, pp.89-102