Professor Jinglai Li PhD

Professor Jinglai Li

School of Mathematics
Professor of Mathematical Optimisation

Contact details

University of Birmingham
B15 2TT

Jinglai Li is a Professor of Mathematical Optimisation at the School of Mathematics of the University of Birmingham. For more information, visit Jinglai's personal webpage.


  • PhD in Mathematics, SUNY Buffalo, 2007
  • BSc in Applied Mathematics, Sun Yat-sen University, 2002


Jinglai Li received the B.Sc. degree in Applied Mathematics from Sun Yat-sen University in 2002 and the PhD degree in Mathematics from SUNY Buffalo in 2007. After his PhD degree, Jinglai did postdoctoral research at Northwestern University (2007-2010) and MIT (2010-2012) respectively.

He subsequently worked at Shanghai Jiao Tong University (Associate Professor, 2012-2017) and University of Liverpool (Reader, 2017-2020). Jinglai joined the University Birmingham as a Professor in Mathematical Optimisation in 2020.

Postgraduate supervision

Jinglai is very happy to discuss PhD project supervision with potential candidates; please email him if you are interested. 


Jinglai Li’s current research interests are in scientific computing, computational statistics, uncertainty quantification, and data science. Specific research topics he has worked on include:

  • Bayesian inverse problems
  • Reliability analysis and rare events simulation
  • Monte Carlo methods
  • Gaussian Process regression and their applications
  • Data assimilation


Sample publications:

Wen, L. and Li, J., 2021. Linear-Mapping based Variational Ensemble Kalman Filter. arXiv preprint arXiv:2103.06315.

Wang, H., Ao, Z., Yu, T. and Li, J., 2021. Inverse Gaussian Process regression for likelihood-free inference. arXiv preprint arXiv:2102.10583.

Yu, T., Wang, H. and Li, J., 2019. Maximizing conditional entropy of Hamiltonian Monte Carlo sampler. arXiv preprint arXiv:1910.05275, SIAM Journal on Scientific Computing, in press.

Zhou, Q., Yu, T., Zhang, X. and Li, J., 2020. Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data. SIAM Journal on Imaging Sciences13(1), pp.29-52.

Wang, H. and Li, J., 2018. Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions. Neural computation30(11), pp.3072-3094.

Hu, Z., Yao, Z. and Li, J., 2017. On an adaptive preconditioned Crank–Nicolson MCMC algorithm for infinite dimensional Bayesian inference. Journal of Computational Physics332, pp.492-503.

Wu, K. and Li, J., 2016. A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification. Journal of Computational Physics321, pp.1098-1109.

Yao, Z., Hu, Z. and Li, J., 2016. A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations. Inverse Problems32(7), p.075006.

Li, J. and Marzouk, Y.M., 2014. Adaptive construction of surrogates for the Bayesian solution of inverse problems. SIAM Journal on Scientific Computing36(3), pp.A1163-A1186.

Li, J., Li, J. and Xiu, D., 2011. An efficient surrogate-based method for computing rare failure probability. Journal of Computational Physics230(24), pp.8683-8697.

View all publications in research portal