Wenjing Wang is a postdoctoral researcher in the Center for Computational Sciences and Engineering (CCSE) in the Computational Research Division of the Computing Sciences Directorate at the Lawrence Berkeley National Laboratory.
She is currently working on developing algorithms for large scale expensive black-box optimization problems and application to High Energy Physics. She is also collaborating with FASTMath SciDAC Institute to develop and deploy scalable mathematical algorithms and software tools for reliable simulation of complex physical phenomena.
She received her PhD in Operations Research from Virginia Tech in May 2019. She is interested in stochastic modeling and simulation, computer experiment design and analysis, and simulation optimization.
» Visit Wenjing's Google Scholar Entry
- X. Chen, W. Wang, G. Xie, R. Hontecillas, M. Verma, A. Leber, P. Lu, J. Bassaganya-Riera and V. Abedi. Multi-resolution sensitivity analysis of model of immune response to Helicobacter pylori infection via spatio-temporal metamodeling. Frontiers in Applied Mathematics and Statistics, 5,4, 2019.
- W. Wang, N. Chen, X. Chen and L. Yang. A variational Bayesian inference-based heteroscedastic Gaussian process approach for simulation metamodeling. ACM Transactions on Modeling and Computer Simulation, 29(1), 6, 2018.
- W. Wang and X. Chen. An adaptive two-stage dual metamodeling approach for stochastic simulation experiments. IISE transactions, 50:9, 820-836, 2018.
- W. Wang and X. Chen. Distributed variational inference-based heteroscedastic Gaussian process metamodeling. Proceedings of the 2019 Winter Simulation Conference, in press.
- W. Wang and X. Chen. The effects of estimation of heteroscedasticity on stochastic kriging. Proceedings of the 2016 Winter Simulation Conference, pp. 326-337. IEEE Press, 2016.