Applied Mathematics

Kun Meng is a Prager Assistant Professor of Applied Mathematics.

(Traditionally, his full name is pronounced "Meng Kun," reminiscent of "Michael" or "Mike" in English. You can address him either by his first name, Kun (pronounced /Kwu:n/), or by his English name, Michael/Mike. He is comfortable with both.)

Education:

Brown University (Providence, RI, USA), 

Ph.D. and Sc.M. in Biostatistics, advisor: Ani Eloyan.

Beijing Normal University (Beijing, China),

M.S. in Pure Mathematics, advisor: Junfeng Li.

Tianjin Polytechnic University (Tianjin, China),

B.S. in Mathematics, advisor: Yonghong Yao.

Research interests and selected manuscripts:

( * indicates the correspondence author; # indicates his advisee.)

1. Topological data analysis: Kun Meng *, Jinyu Wang #, Lorin Crawford, and Ani Eloyan. Randomness of shapes and statistical inference on shapes via the smooth Euler characteristic transform, Journal of the American Statistical Association (Theory and Methods), 2024+
2. fMRI data analysis: Kun Meng * and Ani Eloyan. Population-level task-evoked functional connectivity via Fourier analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024.
3. Manifold learning: Kun Meng * and Ani Eloyan. Principal manifold estimation via model complexity selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2021.
4. Algebraic topology and Euler calculus: Mattie Ji # *, Kun Meng, and Kexin Ding #. Euler characteristics and homotopy types of definable sublevel sets, with applications to topological data analysis. arXiv preprint arXiv:2309.03142, 2023.
5. Dispersive partial differential equations: Junfeng Li and Kun Meng. Global well-posedness for the fifth order Kadomtsev–Petviashvili II equation in three dimensional space. Nonlinear Analysis, 2016.