I am currently a Visiting Member in the Courant Institute of Mathematical Sciences at New York University and a Postdoc Associate at New York University Abu Dhabi, working with Prof. David W. McLaughlin in mathematical and computational neuroscience. I obtained my B.S. in Physics (Zhiyuan College) and Ph.D. degree in Mathematics from Shanghai Jiao Tong University in China under the supervision of Profs. David Cai and Douglas Zhou.
My research interests lie in computational neuroscience, ranging from theoretical study and simulation to data analysis. I have collaborated actively with theoretical and experimental neuroscientists. Directly from experimental data, I have found a common dynamical state (Probability Polling state or p-polling state) underlying neuronal coding. This state is closely related to the Balanced state, linear model in reconstructing neuronal coupling structure (e.g., Granger Causality) and the often-observed weak correlations. I have shown that the p-polling state reveals a mechanism underlying the application of low order Maximum Entropy Principle (MEP) in neuronal networks. My study on the effective interactions of MEP model also shows how a sparse coupling structure can lead to a sparse coding scheme. I also focus on developing methods in analyzing high dimensional neuronal data. I am also interested to borrow ideas from biological neuroscience to study artificial neural network and vice versa. I was involved in the project on studying the theory and application of Granger causality in neuroscience.
You can find my CV here.