Mengyang Gu

Assistant Professor

Department of Statistics and Applied Probability

University of California, Santa Barbara

Santa Barbara, CA

Email: mengyang at pstat.ucsb.edu

I am an assistant professor in the Department of Statistics and Applied Probability at UC Santa Barbara. Previously I was an assistant research professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. Feel free to contact me to work on some interesting research projects together.


  • Uncertainty quantification
  • Bayesian analysis
  • Computer model emulation (numerical methods of partial differential equations)
  • Inverse problem/model calibration
  • Spatio-temporal models
  • functional data analysis
  • Tensor methods
  • Dimension reduction


2016 Ph.D., Statistics, Duke University

2012 B.Sc., Statistics, Zhejiang University; Honor Degree in Chu Kochen Honors College

Current (Fall 2019) course:

PSTAT 120C: Probability and Statistics (in GauchoSpace)

Please find a complete list of publications and preprints in my google scholar profile.

Selected publications & preprints

    1. Anderson, K., Johanson, I., Patrick, M., Gu, M., Segall, P., Poland, M., Montgomery-Brown, E. and Miklius, A. (2019). Magma reservoir failure and the onset of caldera collapse at Kilauea Volcano in 2018. (Accepted in Science).
    2. Gu, M. and Shen, W. (2019). Generalized probabilistic principal component analysis of correlated data. (Accepted in the Journal of Machine Learning Research). (arXiv:1808.10868).
    3. Gu, M., and Xu, Y. (2019). Fast Nonseparable Gaussian Stochastic Process With Application to Methylation Level Interpolation. (Accepted in the Journal of Computational and Graphical Statistics). (arXiv:1711.11501). ("FastGaSP" R package on CRAN).
    4. Gu, M. Jointly robust prior for Gaussian stochastic process in emulation. (2019). calibration and variable selection. Bayesian Analysis, 14(3): 857--885. ("RobustGaSP" R package on CRAN).
    5. Gu, M., Bhattcharjya, D. and Subramanian, D. (2019). Nonparametric estimation of utility functions. (Accepted in AAAI 2020). arXiv:1807.10840.
    6. Gu, M., Wang, X. and Berger, J. (2018). Robust Gaussian stochastic process emulation. Annals of Statistics. 46(6A): 3038--3066. ("RobustGaSP" R package on CRAN).
    7. Gu, M., and Wang, L. (2018) Scaled Gaussian stochastic process for computer model calibration and Prediction. SIAM/ASA Journal on Uncertainty Quantification, 6(4): 1555--1583.
    8. Gu, M., Xie, F. and Wang, L. (2018). A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration. (arXiv:1807.03829). ("RobustCalibration" R package on CRAN).
    9. Gu, M. and Anderson, K. (2018). Calibration of imperfect mathematical models by multiple sources of data with measurement bias. (arXiv:1810.11664).
    10. Gu, M. and Berger, J. (2016). Parallel partial Gaussian process emulation for computer models with massive output. Annals of Applied Statistics, 10(3): 1317--1347. ("RobustGaSP" R package on CRAN, "RobustGaSP in MATLAB" package in Github).


    1. Gu, M., Palomo J. and Berger J. “RobustGaSP” available on CRAN, an R Package for Robust Gaussian Stochastic Process Emulation of complex computer models. arXiv:1801.01874.
    2. Gu, M. RobustCalibration” available on CRAN, an R package for robust calibration for imperfect mathematical models.
    3. Gu, M. "FastGaSP" available on CRAN, an R package for fast and exact computation of Gaussian stochastic process.
    4. Gu, M. "RobustGaSP in MATLAB" package in Github, a MATLAB package for emulating complex computer model.

PhD Thesis

Gu, M. (2016). Robust uncertainty quantification and computation for computer models with massive output. PhD Thesis. Duke University.