• 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.
    • Machine Learning: theory, algorithm and applications;
    • Transport information geometry with applications in data science, statistics, optimization and data-driven scientific computing methods;
    • Numerical Mean field games, Schrodinger equation and Schrodinger Bridge problem.

    Website Links


    • 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 Bayesisan 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