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I received my PhD degree at CIMS, New York University in 2016. My thesis advisor was Professor Andrew J. Majda. 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. I also spent one year as an exchange student in the Department of Scientific Computing of Florida State University.
My research interests lie in the contemporary applied mathematics, fluids and geophysics. Problems with large dimensional, turbulence and partial information are particularly what I am concerned with. Mathematical and physical problems in uncertainty quantification, data assimilation, 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 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 are of great interest to diverse fields such as AOS, 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. I also have experience in mentoring and supervising PhD and master students.
My CV can be found here.
(Last updated 11/22/2017)