# Professor Jinglai Li BSc PhD FIMA

## In 'Staff and Students'

Back to 'School of Mathematics'School of Mathematics

Professor

Head of Statistics and Data Science

## Contact details

- Address
- School of Mathematics

Watson Building

University of Birmingham

Edgbaston

Birmingham

B15 2TT

UK

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

For more information, please visit his personal webpage.

## Qualifications

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

## Biography

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.

## Teaching

### 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.

## Research

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

## Publications

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 Computing*, *32*(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 Sciences*, *13*(1), pp.29-52.

Wang, H. and Li, J., 2018. Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions. *Neural Computation*, *30*(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 Physics*, *332*, pp.492-503.

Wu, K. and Li, J., 2016. A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification. *Journal of Computational Physics*, *321*, 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 Problems*, *32*(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 Computing*, *36*(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 Physics*, *230*(24), pp.8683-8697.