I will be joining the School of Computational Science and Engineering (CSE), Georgia Institute of Technology in Spring 2022. If you are interested in working on machine learning and computational neuroscience with me, please directly reach out to me.
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 Computational and Quantitative Neuroscience with a graduate certificate in Statistics and Machine Learning from 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.
I also worked as a research summer intern at Machine Intelligence and Perception, Microsoft Research Cambridge in the UK, under the supervision of Dr. Sebastian Nowozin.
Here is my CV.
My research interest is to develop scientifically-motivated probabilistic modeling approaches for neural and behavior analyses, and scalable and efficient inference algorithms to fit the models. Specifically, my 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.
(vi) deep learning and probabilistic modeling for behavior tracking and analysis.
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".
Here is an oral presentation Roger She gave at UAI 2019, Israel. It's a collaborative work between Roger and me on "Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks".