Olivier Zahm

Research scientist at Inria

Short bio:

  • 2018-now: Research scientist (chargé de recherche) at Inria-Grenoble in the AIRSEA team.
  • 2015-2018: Postdoctoral associate at MIT in the UQGroup.
  • 2012-2015: Ph.D. at École Centrale Nantes.
  • 2008-2012: Bachelor and Master program at École Normale Supérieure Cachan.


(link to the group meeting)

Job offers


  1. Tiangang Cui and Olivier Zahm: Data-free likelihood-informed dimension reduction of Bayesian inverse problems, preprint 2020. hal
  2. Michael Brennan, Daniele Bigoni, Olivier Zahm, Alessio Spantini, Youssef Marzouk: Greedy inference with structure-exploiting lazy maps, preprint 2020. arXiv
  3. Kathrin Smetana, Olivier Zahm: Randomized residual-based error estimators for the Proper Generalized Decomposition approximation of parametrized problems, preprint 2019. arXiv
  4. Rémi Lam, Olivier Zahm, Youssef Marzouk and Karen Willcox: Multifidelity Dimension Reduction via Active Subspaces, preprint 2018. arXiv.
  5. Kathrin Smetana, Olivier Zahm and Anthony T. Patera: Randomized residual-based error estimators for parametrized equations, SIAM-SISC, 41(2), p. A900–A926, 2019. link, arXiv.
  6. Olivier Zahm, Tiangang Cui, Kody Law, Alessio Spantini and Youssef Marzouk: Certified dimension reduction in nonlinear Bayesian inverse problems, preprint 2018. arXiv
  7. Olivier Zahm, Paul Constantine, Clémentine Prieur and Youssef Marzouk: Gradient-based dimension reduction of multivariate vector-valued functions, preprint 2018. arXiv.
  8. Weiqi Ji, Jiaxing Wang, Olivier Zahm, Youssef Marzouk, Bin Yang, Zhuyin Ren and Chung K. Law: Shared low-dimensional subspaces for propagating kinetic uncertainty to multiple outputs, Combustion and Flame, Volume 190, p. 146-157, 2017. link.
  9. Olivier Zahm, Marie Billaud-Friess and Anthony Nouy: Projection based model order reduction methods for the estimation of vector-valued variables of interest, SIAM-SISC 39(4), p. A1647-A1674, 2015. link, arXiv.
  10. Olivier Zahm and Anthony Nouy: Interpolation of inverse operators for preconditioning parameter-dependent equations, SIAM-SISC, 38(2), p. A1044-A107, 2016. link, arXiv.
  11. Paul Cazeaux and Olivier Zahm: A fast boundary element method for the solution of periodic many-inclusion problems via hierarchical matrix techniques, ESAIM:Proc, Vol. 48, p. 156-168, 2015. link.
  12. Marie Billaud-Friess, Anthony Nouy and Olivier Zahm: A tensor approximation method based on ideal minimal residual formulations for the solution of high-dimensional problems, ESAIM: M2AN 48, p. 1777-1806, 2014. link, arXiv.

PhD thesis (supervisor: Anthony Nouy and Marie Billaud-Freiss):

Model order reduction methods for parameter-dependent equations: Applications in Uncertainty Quantification, 2015. link.

Teaching: « Model Exploration for Approximation of Complex, High-Dimensional Problems » (MSIAM)

  • Introductory slides (link)
  • Introduction to sensitivity analysis (slides)
  • List of articles (link)
  • Lecture notes on Projection-based Model Order Reduction techniques (link)
  • Practical session on Projection-based Model Order Reduction techniques (link)
  • Exercises on Projection-based Model Order Reduction techniques (link)
  • Practical session on Polynomial least-squares (link)
  • For the next practical session, make sure you’ve installed the following Rstudio packages on your laptop (if you’re using your laptop…):
    • DiceKriging
    • lhs
    • DiceView
    • sensitivity
    • boot
    • numbers
  • Gaussian processes (link)
  • Last year exam (link, correction)

Teaching: « Spatial statistics » (SSD)

  • Practical session 1 (link)
  • Practical session 2 (link)

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