Recent Group news: 

• May 2024: We congratulate PhD student Xinyi Fang on being awarded the Graduate Division Dissertation Fellowship. 

• Mar 2024: We congratulate PhD students Xubo Liu and Blythe King on getting summer internship offers from Meta and Johnson & Johnson, respectively. 

• Feb 2024:  We congratulate Hanmo Li for successfully defending his PhD thesis! 

• Feb 2024: PSTAT PhD students Xinyi Fang and Yizi Lin are awarded Heeger Fellowships. Congratulations, Xinyi and Yizi!

• Jan 2024: Our proposed topic-contributed session in 2024 Joint Statistical Meetings (JSM), "Advances in Statistical Learning and Uncertainty Quantification: Theory and Computation" is accepted.  Please join us at Oregon, Portland in August 2024.

• Jan 2024: Our group will give three presentations in 2024 American Physical Society (APS) March Meeting, please find the Abstract 1, Abstract 2, Abstract 3. In addition,  Our collaborator, Yimin Luo from Yale is going to give an invited presentation of our collaborative article from PRX Life in APS March Meeting.  Please join us at Minneapolis, Minnesota in March 2024. 

• Oct 2023: Mengyang Gu was selected as a Scialog fellow for the meeting of automating chemical laboratories by Research Corporation for Science Advancement.

• Oct 2023:  The preprint, "Sequential Kalman filter for fast online changepoint detection in longitudinal health records" is available on arXiv.  

• Oct 2023: We congratulate Hao Li for successfully defending his PhD thesis! 

• Oct 2023: Our paper, "RobustCalibration: Robust Calibration of Computer Models in R", is accepted in The R Journal for publication. 

• Sep 2023: our paper, "Data-Driven Model Construction for Anisotropic Dynamics of Active Matter" has been selected by PRX Life as one of five spotlight invited presentations in APS March Meeting in 2024.

• Sep 2023: The preprint, "ab initio uncertainty quantification in scattering analysis of microscopy", is available on arXiv. (Slides; Video of presentation at Harvard AstroStat). 

• Sep 2023: Our paper, "Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification" has been accepted in Physica D: Nonlinear Phenomena" for publication.

• July 2023: We organized the joint workshop of UC Collaborative for AI-enabled Materials Exploration and Optimization (UC-CAMEO) and Collaboration Strengthening through Data Science (CSDS) for 20 participants from 4 UC campuses on July 17-18. Here is the agenda of the workshop. 

• July 2023: Our paper, "Data-Driven Model Construction for Anisotropic Dynamics of Active Matter", is accepted in Physical Review X Life for publication. 

• June 2023: Our collaborative paper, "Molecular-scale substrate anisotropy, crowding and division drive collective behaviours in cell monolayers", is accepted in Journal of Royal Society Interface for publication.

• April 2023: Our collaborative paper, "High-throughput microscopy to determine morphology, microrheology, and phase boundaries applied to phase separating coacervates", is selected as one of the Soft Matter Editorial Board Highlights of 2022. 

• Feb 2023: Our paper, "Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias", was accepted by the Technometrics for publication. 

• Jan 2023: Our collaborative project, "UC Collaborative for AI-enabled Materials Exploration and Optimization (UC-CAMEO)" is awarded as a result of the UC Multicampus Research Programs and Initiatives competition.

• Nov 2022: Our paper, "Reliable emulation of complex functionals by active learning with error control" was accepted by the Journal of Chemical Physics for publication.

• Oct 2022: The preprint, "Molecular-scale substrate anisotropy and crowding drive long-range nematic order of cell monolayers" is available on arXiv.  

• Oct 2022: Our collaborative paper, "The Importance of Contextualization of Crowdsourced Active Speed Test Measurements", received the Best Long Paper Award from the ACM Internet Measurement Conference (IMC) in 2022. 

• Sep 2022: We organized the kick-off workshop of the Collaboration Strengthen through Data Science initiative on Sep 12. Here is the Agenda of the Workshop.

• Sep 2022: Our paper, "Scalable marginalization of correlated latent variables with applications to learning particle interaction kernels" was accepted by the New England Journal of Statistics in Data Science for publication. 

• 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: 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.

• 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.

• Apr 2021: Our paper, "Robust estimation of SARS-CoV-2 epidemic in US counties" was accepted in Scientific Reports. 

• 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.

Recent publications 

• 1. 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).

  2. 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).

  3. 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.

  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., 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).

  6. 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).

  7. Gu, M., Bhattcharjya, D. and Subramanian, D. (2020). GaSPing on Utility. AAAI 2020.

  8. Gu, M. and Shen, W. (2020). Generalized probabilistic principal component analysis of correlated data. Journal of Machine Learning Research, 21: 1-41.

  9. Wu, J. and Gu, M. (2020). Emulating the first principles of matter: a probabilistic roadmap. Springer, arXiv preprint arXiv:1708.04753.

  10. Li, H. and Gu, M. (2020). Robust estimation of SARS-CoV-2 epidemic at US counties. Scientific reports, 11 (1), 1-16.

  11. Gu, M. and Li, H. (2020). Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data. Accepted in Bayesian Analysis, arXiv preprint arXiv:2011.10863.

  12. 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.

  13. Luo, Y., Gu, M., Edwards, C. E., Valentine, M. T., and Helgeson, M. E. (2022). High-throughput microscopy to determine morphology, microrheology, and phase boundaries applied to phase separating coacervates. Soft Matter, 18 (15), 3063-3075.

  14. Li, H., Zhou, M., Sebastian, J., Wu, J., and Gu, M. (2022). Efficient force field and energy emulation through partition of permutationally equivalent atoms. The Journal of Chemical Physics, 156 (18), 184304.

  15. Gu, M., Liu, X., Fang, X., & Tang, S. (2022). Scalable marginalization of latent variables for correlated data. Accepted in the New England Journal of Statistics in Data Science. arXiv preprint arXiv:2203.08389

  16. Gu, M., Xie, F., & Wang, L. (2022). A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration. Accepted in SIAM/ASA Journal of Uncertainty quantification. arXiv preprint arXiv:1807.03829.

Software

• 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. Manuscript available in R Journal.

  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.

  5. 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.

PhD Thesis

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