Received his Bachelor’s degree in Applied Mathematics from the Sun Yat-Sen University in 2002 and his Ph.D degree in mathematics from the State University of New York at Buffalo in 2007. In 2007-2010, he did postdoctoral research in the department of Engineering Science and Applied Mathematics of the Northwestern University. In 2010, he moved to the Massachusetts Institute of Technology, working as a postdoctoral associate at the Department of Aeronautics and Astronautics. He joined the faculty of the Shanghai Jiaotong University in 2012, where he is now a distinguished research fellow at the Institute of Natural Science. He is also holding a visiting faculty position at MIT. Li’s main research interests are scientific computing, computational statistics, uncertainty quantification, as well as the applications in various scientific and engineering problems. His mathematical interests lie on the interface between statistical science and scientific computing. Jinglai Li has published in several scientific journals including the SIAM Journal of Applied Mathematics, Journal of Computational Physics, and Optics Letters. Jinglai Li has also been invited to speak in numerous conference and workshops such as the SIAM Annual Meeting, SIAM Conference of Uncertainty Quantification, SIAM Conference of Computational Science and Engineering.
Scientific Computing, Uncertainty Quantification, Inverse Problems, Data Assimilation, Rare Events, Stochastic, Nonlinear PDEs, Optics, etc.
Z. Hu, Z. Yao and J. Li, On an adaptive preconditioned Crank–Nicolson MCMC algorithm for infinite dimensional Bayesian inference, Journal of Computational Physics 332 (2017).
T. Gao and J. Li, A derivative free trust region method for reliability based optimizations, Structural and Multidisciplinary Optimization 55 (2017).
K. Wu and J. Li, A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification, Journal of Computational Physics 321 (2016).
Z. Yao, Z. Hu and J. Li, A TV-Gaussian prior for infinite dimensional Bayesian inverse problems and its numerical implementations, Inverse Problems 32 (2016).
J. Li and W.L. Kath, A path-based method for simulating large deviations and rare events in nonlinear lightwave systems, Studies in Applied Mathematics 136 (2016).
H. Wang, G. Lin and J. Li, Gaussian process surrogates for failure detection: a Bayesian experimental design approach, Journal of Computational Physics 313 (2016).
J. Li, G. Lin and X. Yang, A frozen Gaussian approximation based multi-level particle swarm optimization for seismic inversion, Journal of Computational Physics 296 (2015).
J. Li and Y.M. Marzouk, Adaptive construction of surrogates for the Bayesian solution of inverse problems, SIAM Journal on Scientific Computing 36 (2014).