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I received my PhD degree from the Courant Institute of Mathematical Sciences (CIMS) and the Center of Atmosphere and Ocean Science (CAOS), New York University (NYU) in May 2016. After that, I was a postdoc research associate at CIMS, NYU from June 2016 to May 2018. My undergraduate major was Mechanical Engineering, Fudan University in Shanghai and I received my Master's degree at The School of Mathematical Sciences Fudan University.
My research interests lie in the contemporary applied mathematics, geophysics, applied stochastics, algorithms and data science. Problems with large dimensional, turbulence and partial information are particularly what I am concerned with. Mathematical and physical problems in uncertainty quantification (UQ), data assimilation, infromation theory, applied stochastic analysis, inverse problems, high-dimensional data analysis and effective prediction all belong to my research topics. I am also devoted to proposing efficient and statistically accurate algorithms to ameliorate the curse of dimensionality for large-dimensional complex dynamical systems with strong non-Gaussian features. In addition, I'm active in developing both dynamical and stochastic models and use these models to predict real-world phenomena related to atmosphere ocean science, climate and other complex systems such as the Madden-Julian Oscillation (MJO), the monsoon and the El Nino Southern Oscillation (ENSO) based on real observational data.
The mathematical and computational tools developed in my work can be of great interest to diverse fields such as atmosphere ocean science, climate, material science, neuroscience, excitable media, physics and engineering.
I have teaching experience for different courses ranging from undergraduate to graduate levels, including Calculus, Uncertainty Quantification, Filtering Complex Turbulent Dynamical Systems, and Simple Dynamical Stochastic Models for the ENSO.
My CV can be found here.
(Last updated 08/06/2019)
Available postdoc position: https://www.mathjobs.org/jobs/jobs/14230