I am a Ph.D. candidate in Neuroscience institute at Princeton University where I am advised by Prof. Jonathan Pillow.

I completed my M.S. in Computer Science at University of Southern California and B.S. in Electrical Engineering at Harbin Institute of Technology.

Prior to Princeton, I worked as a research associate for a summer at University of Texas at Austin and Princeton Neuroscience Institute with Jonathan Pillow.

My research interest focuses on Bayesian statistical models of high-dimensional and large-scale neural response and fMRI decoding.

My Ph.D work lies in:

(i) sparse Bayesian structure learning for fMRI decoding with hierarchical generative models,

(ii) fast moment-based convolutional spike-triggered covariance analysis for neural sensory encoding,

(iii) Bayesian optimization and active learning,

(iv) Bayesian nonparametric latent variable models for large-scale neural recordings,

(v) Bayesian neural networks and approximate Bayesian inference.

Moreover, my interest also spans many data-driven research using statistical and machine learning tools for efficiently analyzing real world data, e.g. neural data, time series, geospatial data, speech data.