I am a Postdoctoral Research Fellow at the Center for Theoretical Neuroscience at the Zuckerman Mind Brain Behavior Institute at Columbia University, working with Prof. Liam Paninski and Prof. John Cunningham.
I completed my PhD in Neuroscience institute at Princeton University where I was 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.
Here is an oral presentation I gave at ICLR 2019, New Orleans. The topic is 'Deterministic variational inference for robust Bayesian neural networks'.
Here is an oral presentation I gave at Cosyne 2019, Lisbon. The topic is 'Learning a latent manifold of odor representations in piriform cortex'.