• Short biography

    • I am moving to University of South Carolina as an assistant professor.
    • UCLA, CAM assistant professor in Mathematics, 2016-2020.
    • Georgia Tech, Ph.D. in Mathematics, 2016.
    • Georgia Tech, M.S. in Statistics, 2015.
    • Shandong University, B.S.in Mathematics, 2009.

    Research areas

      Google Scholar.
    • Transport information geometry with applications in Data science;
    • Numerical Mean field games, optimal transport, Schrodinger equation and Schrodinger Bridge problem.

    Website Links


    • Draft "Hessian metric via transport information geometry" is online. We extends and contains the classical optimal transport metric to the Hessian metrics. We observe that there are several connections with math physics equations. In particular, the transport Hessian Hamiltonian flow of negative Botlzmann Shannon equation satisfies the Shallow Water's equation; The transport Hessian metric is a particular mean field Stein metric. The new metrics would be useful in designing Bayesian sampling algorithms and AI inferences. March 23, 2020. [PDF]
    • Our paper "A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems" has been accepted in PNAS. March, 2020.
    • Draft "Neural Fokker--Planck equation" is online. We propose Fokker-Planck equations within machine learning generative models. This approach allows us tp approximate high dimensional Fokker-Planck equations. A neural kernelized mean field stochastic differential equation is proposed. Numerical examples and analysis are provided. February 26, 2020. [PDF]
    • Draft "Neural primal dual method" is online. We combine the generative adversary networks and primal dual algorithms to solve general mean field games. Here the primal (density) and dual (potential) variables are approximated by generators and discriminators, respectively. This methods allows us to approximate the solution includings 50 dimension. Feb 24, 2020. [PDF]
    • Draft "Information Newton flow: second-order optimization method in probability space" is online. We propose information Newton's flows for sampling optimization problems arised in inverse problem and Bayesian statistics. Newton's Langvein dynamics, a.k.a Wasserstein Newton's flows of KL divergence, are derived. We propose second-order algorithms for Bayesian sampling problems. Jan 13, 2020. [PDF]
    • Paper "Ricci curvature for parametric statistics via optimal transport" has been accepted in Information Geometry. January 12, 2020.
    • Paper "Kernelized Wasserstein Natural Gradient" is accepted in ICLR 2020, Spotlight. Dec 20, 2019.
    • Draft "A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems" is online. We provide a computational method to approximate solutions of high dimenisonal potential mean field games. Dec 4, 2019. [PDF]
    • Draft "Wasserstein information matrix" is online. We study statistical and estimation properties of Wasserstein informatin matrices. The Wasserstein score function, the Wasserstein-Cramer-Rao bound, the Wasserstein efficiency and the Poincare efficiency (interplay with Wasserstein and Fisher information matrices) are derived. Oct 24, 2019. [PDF]


    Information Newton's flow [Talk ] [Paper]
    Wasserstein Information Matrix [Talk ] [Paper1]
    Mean Field Games with Applications. [Overview Talk ] [Generative network Talk]
    Accelerated Information Gradient flow. [Talk ] [pdf ]
    Unnormalized Optimal Transport. [Talk ] [pdf1][pdf2]
    Transport information geometric learning. [Talk ] [pdf1 ][pdf2] [pdf3] [pdf4][ pdf5 ][ pdf6 ]
    Evolutionary games via optimal transport. [Talk ] [pdf1 ][pdf2 ]
    Computational Mean field games. [Talk ] [pdf ]
    Hamiltonian flows via optimal transport. [Talk1] [Talk2] [pdf1 ][pdf 2][ pdf3] [pdf4]
    Gradient flows on graphs via optimal transport. [Talk ] [pdf1 ] [pdf2 ]
    L1 optimal transport with applications. [Talk ] [pdf1] [pdf2] [pdf3] [pdf4][pdf5]


    • Office: MS 7620F, UCLA.
    • Email: wcli at math.ucla.edu