Tiangang Cui

tcui2.jpg

Office 489

School of Mathematics and Statistics F07

University of Sydney, NSW 2006

Research: Google Scholar, ORCID, Code

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

Brief Biography:

  • 2023–present: 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

Jan 7, 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 3, 2023 Starting a new job at the School of Mathematics and Statistics at U Syd.
Aug 20, 2023 Co-organizing the Theoretical and Computational Advances in Measure Transport workshop at the 10th International Congress on Industrial and Applied Mathematics (ICIAM) in Tokyo, Japan.

selected publications

  1. JMLR
    Tensor-train methods for sequential state and parameter learning in state-space models
    Zhao, Yiran, and Cui, Tiangang
    Journal of Machine Learning Research, 25, 1–51, 2024 arXiv
  2. SISC
    Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation
    Cui, Tiangang, Dolgov, Sergey, and Scheichl, Robert
    SIAM Journal on Scientific Computing, C1–C29, 2024 DOI arXiv
  3. JCP
    Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction
    Cui, Tiangang, Dolgov, Sergey, and Zahm, Olivier
    Journal of Computational Physics, 112103, 2023 DOI arXiv
  4. MathComp
    Certified dimension reduction in nonlinear Bayesian inverse problems
    Zahm, Olivier, Cui, Tiangang, Law, Kody, Spantini, Alessio, and Marzouk, Youssef
    Mathematics of Computation, 91, 1789–1835, 2022 DOI arXiv
  5. Bernoulli
    A unified performance analysis of likelihood-informed subspace methods
    Cui, Tiangang, and Tong, Xin T
    Bernoulli, 28, 2788–2815, 2022 DOI arXiv
  6. FoCM
    Deep composition of tensor trains using squared inverse Rosenblatt transports
    Cui, Tiangang, and Dolgov, Sergey
    Foundations of Computational Mathematics, 22, 1863–1922, 2022 DOI arXiv
  7. JUQ
    Optimization-based Markov chain Monte Carlo methods for nonlinear hierarchical statistical inverse problems
    Bardsley, Johnathan M, and Cui, Tiangang
    SIAM/ASA Journal on Uncertainty Quantification, 9, 29–64, 2021 DOI arXiv
  8. IJNME
    A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems
    Cui, Tiangang, Fox, Colin, and O’Sullivan, Michael J
    International Journal for Numerical Methods in Engineering, 118, 578–605, 2019 DOI arXiv
  9. NIPS
    A Stein variational Newton method
    Detommaso, Gianluca, Cui, Tiangang, Marzouk, Youssef, Spantini, Alessio, and Scheichl, Robert
    In Advances in Neural Information Processing Systems 9169–9179, 2018 arXiv
  10. JCP
    Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
    Cui, Tiangang, Marzouk, Youssef M, and Willcox, Karen E
    Journal of Computational Physics, 315, 363–387, 2016 DOI arXiv
  11. JCP
    Dimension-independent likelihood-informed MCMC
    Cui, Tiangang, Law, Kody JH, and Marzouk, Youssef M
    Journal of Computational Physics, 304, 109–137, 2016 DOI arXiv