I am an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. In general my research interest includes Bayesian machine learning, deep learning and deep reinforcement learning. Specifically, I am currently interested in:
Previously, I was a Research Assistant Professor and a Postdoctoral Associate in the Department of Electrical and Computer Engineering at Duke University. I got my PhD from College of Engineering and Computer Science, the Australian National University in 2014, and my master and bachelor degrees both from School of Computer Science, Fudan University, Shanghai, China.
My CV (outdated) can be downloaded here.
I will serve as an Area Chair for NeurIPS 2021.
I will serve as an Area Chair for IJCAI 2021.
Two papers accepted by ICLR 2019.
Two papers accepted by NeurIPS 2019.
One paper accepted by IROS 2018.
One paper accepted by UAI 2018.
Two papers accepted by ICML 2018.
Two papers accepted by AISTATS 2018.
My current research interest focuses on scalable Bayesian methods and deep learning. Specially, I am interested in developing algorithms and theory for stochastic gradient Markov Chain Monte Carlo, deep generative models, as well as understanding insights of deep learning with Bayesian solutions.
I am also interested in Bayesian Nonparametrics, including but not limited to Poisson processes, Dirichlet processes, Indian buffet processes, Gaussian processes, and the family of dependent normalized random measures – my PhD thesis topic. For applications, I focus on Text Mining and Topic Models.
Check out my Publication page for details.