Assistant Professor
Institute of Mathematical Sciences
ShanghaiTech University
No. 393 Middle Huaxia Road
Pudong New District
Shanghai 201210 China

Contact Information

• Office: Room 515, Southern Building of School of Creativity & Arts
• Email: jiangshx@shanghaitech.edu.cn

Research interests

• Manifold Learning, Kernel Based Approaches, Diffusion Maps, Radial Basis function
  

• Nonparametric Modeling, Parameter Estimation, Modeling Order Reduction
  

• Nonlinear Dispersive Waves, Internal Waves, Fermi-Pasta-Ulam Chains
  

Short Bio

• Tenure-track Assistant Professor (2020.09-Present)
    Institute of Mathematical Sciences, ShanghaiTech University.
• Assistant Research Professor (2018.08-2020.08)
    Department of Mathematics, the Pennsylvania State University, USA.
    Mentor: John Harlim.
  
• Postdoc research associate (2017.08-2018.06)
    Department of Mathematics, the Pennsylvania State University, USA.
    Mentor: John Harlim.
  
• Ph.D. in Applied Mathematics (2017.06)
    School of Mathematical Sciences and Institute of Natural Sciences,
    Shanghai Jiao Tong University, China.
    Advisors: David Cai and Douglas Zhou.
  
• B.S. in Applied Mathematics (2010.06)
    Department of Mathematics, Shanghai Jiao Tong University, China.

Recruitment

I plan to recruit Chinese graduate students (Chinese students only) and international postdocs (international allowed for postdocs) on these projects:

• solving linear and nonlinear PDEs on unknown manifolds using diffusion maps, radial basis functions, deep neural networks,
  

• nonparametric modeling, bayesian inference, model order reduction using diffusion maps and deep neural network,
  

• stochastic linearization of FPU chains, numerical simulation of two-layer fluid system.
  

The projects are not limited to the above ones and you are encouraged to propose your own ideas.

Students with the background of applied mathematics or computer science or any related subject are welcome to contact me. Ones are required to be familiar with at least one of the following computer skills, Matlab, Python, or C/C++. During the start of graduate studies, students are encouraged to learn courses including numerical analysis, deep neural networks, Riemannian manifold, and finite element method, etc.