Mengyang Gu's Homepage
Recent Group news:
May 2022: Mengyang Gu was selected as one of the recipients of the Hellman Family Faculty Fellowship for the 2022-2023 academic year.
Apr 2022: We organized a minisymposium (part I and part II) in SIAM Conference on Uncertainty Quantification (UQ 22) and Mengyang Gu gave the SIAG/Uncertainty Quantification Early Career Prize Lecture.
Apr 2022: Our paper, "A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration", is accepted by SIAM/ASA Journal of Uncertainty Quantification for publication.
Apr 2022: Our paper, "Efficient force field and energy emulation through partition of permutationally equivalent atoms", is accepted by Journal of Chemical Physics.
Mar 2022: The preprint, "Scalable marginalization of latent variables for correlated data", is available on arXiv.
Mar 2022: Our in-house research proposal, "Automated cell tracking, velocity field and orientation analysis" was awarded by NSF BioPACIFIC MIP.
Mar 2022: Hanmo Li was selected to receive the NSF mathematical science graduate internship (MSGI) and he will join Lawrence Livermore National Laboratory as a research intern this summer.
Feb 2022: We are hiring a postdoc scholar (joint with Prof. Yuedong Wang) in COVID-19 Prediction Models and the next review date is April 15, 2022.
Jan 2022: Xinyi Fang was selected as the winner of the Abraham Wald Memorial Award (best performance in qualified exams at PSTAT).
Jan 2022: The preprint, "RobustCalibration: Robust Calibration of Computer Models in R" is available on arXiv.
Oct 2021: Mengyang Gu was selected as the recipient of SIAM Activity Group on Uncertainty Quantification Early Career Prize in 2022.
Oct 2021: Our paper, "Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data", is accepted by Bayesian Analysis.
Sep 2021: Our paper, "Uncertainty quantification and estimation in differential dynamic microscopy" is accepted by Physical Review E for publication.
Sep 2021: Mengyang Gu joined the Editorial Board of Data Science in Science as an AE.
Aug 2021: The preprint, "Efficient force field and energy emulation through partition of permutationally equivalent atoms", is available on arXiv.
May 2021: The preprint, "Uncertainty quantification and estimation in differential dynamic microscopy", is available on arXiv.
Apr 2021: Our paper, "Robust estimation of SARS-CoV-2 epidemic in US counties" was accepted in Scientific Reports. We established the COVID-19 U.S. County-Level Dashboard in Oct 2020 based on this work.
Mar 2021: Our project, "Collaborative Research: Fusing Massive Disparate Data and Fast Surrogate Models for Probabilistic Quantification of Uncertain Hazards" was awarded by NSF.
Jan 2021: Our in-house research proposal, "Optimization of DDM-based Microrheology Analysis through Gaussian Process Regression" was awarded by NSF BioPACIFIC MIP.
Dec 2020: Yue He was selected as a fellow of BioPACIFIC MIP for winter 2021.
Computer model emulation (numerical methods of partial differential equations)
Inverse problem/model calibration
Natural hazard assessment
Please find a complete list of publications and preprints in my google scholar profile.
Selected publications & preprints
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).
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.
Gu, M., Xie, F. and Wang, L. (2018). A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration. arXiv preprint arXiv:1807.03829. ("RobustCalibration" R package on CRAN).
Gu, M. and Anderson, K. (2018). Calibration of imperfect mathematical models by multiple sources of data with measurement bias. arXiv preprint, arXiv:1810.11664.
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).
Gu, M., and Xu, Y. (2019). Fast Nonseparable Gaussian Stochastic Process With Application to Methylation Level Interpolation. Journal of Computational and Graphical Statistics, 29: 250-260. ("FastGaSP" R package on CRAN).
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. Science, 366 (6470). ("RobustGaSP-in-MATLAB" package on Github).
Gu, M., Bhattcharjya, D. and Subramanian, D. (2020). GaSPing on Utility. AAAI 2020.
Gu, M. and Shen, W. (2020). Generalized probabilistic principal component analysis of correlated data. Journal of Machine Learning Research, 21: 1-41.
Wu, J. and Gu, M. (2020). Emulating the first principles of matter: a probabilistic roadmap. arXiv preprint arXiv:1708.04753.
Li, H. and Gu, M. (2020). Robust estimation of SARS-CoV-2 epidemic at US counties. (Accepted in Scientific Reports). arXiv preprint arXiv:2010.11514.
Gu, M. and Li, H. (2020). Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data. Bayesian Analysis, arXiv preprint arXiv:2011.10863.
Gu, M., Luo, Y., He, Y., Helgeson, M. and Valentine, M. (2021). Uncertainty quantification and estimation in differential dynamic microscopy. Physical Review E, 104: 034610.
Gu, M. “RobustCalibration” available on CRAN, an R package for robust calibration for imperfect mathematical models.
Gu, M. "FastGaSP" available on CRAN, an R package for fast and exact computation of Gaussian stochastic process.
Gu, M. "RobustGaSP in MATLAB" package in Github, a MATLAB package for emulating complex computer model.
Yue He and Gu, M. "DDM-UQ" package in Github, a MATLAB package for efficiently extracting information from microscopic videos and simulating 2D particle movements.
Gu, M. (2016). Robust uncertainty quantification and computation for computer models with massive output. PhD Thesis. Duke University.