T Cui

tcui2.jpg

Research: Google Scholar, ORCID, Code

computational mathematics, machine learning, inverse problems, numerical analysis, geophysics, and scientific computing

Brief Biography:

  • 2026–present: Associate Professor, School of Mathematics and Statistics, University of Sydney
  • 2026–2029: ARC Future Fellow, School of Mathematics and Statistics, University of Sydney
  • 2023–2025: Senior Lecturer, School of Mathematics and Statistics, University of Sydney
  • 2016–2023: Lecturer, Senior Lecturer, School of Mathematics, Monash University
  • 2015–2016: Senior Research Engineer, ExxonMobil Upstream Research Company
  • 2012–2015: Postdoc Associate, Massachusetts Institute of Technology

Contact: tiangang(dot)cui(at)sydney(dot)edu(dot)au

news

Jun 10, 2026 HDA2027: 11th Workshop on High-Dimensional Approximation
Jan 07, 2025 We will run the 3rd New Zealand Workshop on Uncertainty Quantification and Inverse Problems at the University of Auckland from 18 Feb to 21 Feb, 2025.
Dec 10, 2024 Looking forward to visiting Oden Institute for Computational Engineering and Sciences at UT Austin as a JTO Faculty Fellow in Autumn 2025.
Aug 10, 2024 Looking forward to visiting HGS MathComp at Heidelberg University again from 15 Sep to 2 Nov. I will offer a week-long course on Computational Linear Algebra for Machine Learning.
Oct 03, 2023 Starting a new job at the School of Mathematics and Statistics at U Syd.

selected publications

  1. JCP
    Dimension-independent likelihood-informed MCMC
    Tiangang Cui, Kody JH Law, and Youssef M Marzouk
    Journal of Computational Physics, 2016
    DOI arXiv
  2. JCP
    Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
    Tiangang Cui, Youssef M Marzouk, and Karen E Willcox
    Journal of Computational Physics, 2016
    DOI arXiv
  3. IJNME
    A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems
    Tiangang Cui, Colin Fox, and Michael J O’Sullivan
    International Journal for Numerical Methods in Engineering, 2019
    DOI arXiv
  4. JUQ
    Optimization-based Markov chain Monte Carlo methods for nonlinear hierarchical statistical inverse problems
    Johnathan M Bardsley and Tiangang Cui
    SIAM/ASA Journal on Uncertainty Quantification, 2021
    DOI arXiv
  5. Bernoulli
    A unified performance analysis of likelihood-informed subspace methods
    Tiangang Cui and Xin T Tong
    Bernoulli, 2022
    DOI arXiv
  6. MathComp
    Certified dimension reduction in nonlinear Bayesian inverse problems
    Olivier Zahm, Tiangang Cui, Kody Law, and 2 more authors
    Mathematics of Computation, 2022
    DOI arXiv
  7. FoCM
    Deep composition of tensor trains using squared inverse Rosenblatt transports
    Tiangang Cui and Sergey Dolgov
    Foundations of Computational Mathematics, 2022
    DOI arXiv
  8. NIPS
    A Stein variational Newton method
    Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, and 2 more authors
    In Advances in Neural Information Processing Systems, 2018
    arXiv
  9. JCP
    Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction
    Tiangang Cui, Sergey Dolgov, and Olivier Zahm
    Journal of Computational Physics, 2023
    DOI arXiv
  10. SISC
    Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation
    Tiangang Cui, Sergey Dolgov, and Robert Scheichl
    SIAM Journal on Scientific Computing, 2024
    DOI arXiv
  11. JMLR
    Tensor-train methods for sequential state and parameter learning in state-space models
    Yiran Zhao and Tiangang Cui
    Journal of Machine Learning Research, 2024
    arXiv