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.