Professor Jinglai Li BSc PhD FIMA

Professor Jinglai Li

School of Mathematics
Head of Statistics and Data Science

Contact details

School of Mathematics
Watson Building
University of Birmingham
B15 2TT

Jinglai Li is a Professor at the School of Mathematics of the University of Birmingham.

For more information, please visit his 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 2020.


Semester 1

LM Topics in Applied Mathematics

Semester 2

LI/LH Statistics

Postgraduate supervision

I am happy to discuss PhD project supervision with potential candidates, so please email me if you are interested. 


Jinglai Li’s current research interests are in scientific computing, computational statistics, uncertainty quantification, and data science. 

Research Themes

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


Sample publications:

Cheng, C. and Li, J., 2022. ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics. arXiv preprint arXiv:2210.09602.

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

Ao, Z. and Li, J., 2023. Entropy estimation via uniformization. Artificial Intelligence, p.103954.

Wen, L. and Li, J., 2022. Affine-mapping based variational ensemble Kalman filter. Statistics and Computing32(6), pp.1-15.

Ao, Z. and  Li, J., 2021, Entropy estimation via normalizing flow. in Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022). 

Yu, T., Wang, H. and Li, J., 2021. Maximizing conditional entropy of Hamiltonian Monte Carlo sampler. SIAM Journal on Scientific Computing, 43(5), pp.A3607–A3626.

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