I am a PhD student (since 2015) in the Department of Computer Science at Tsinghua University, advised by Jun Zhu. I have broad interests in probabilistic methods and approximate Bayesian inference, including and not limited to these topics: probabilistic kernel methods (e.g., Gaussian processes), spectral methods, variational inference, generative models, and Bayesian deep learning.
I’m currently a resesarch intern at DeepMind, London. Previously I was an intern at RIKEN-AIP, Tokyo. I created and lead the development of ZhuSuan, an open-source probabilistic programming library. I was awarded the Microsoft Research Asia Fellowship for 2018. I received my B.E. from the Department of Computer Science and Technology at Tsinghua University.
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.
Functional Variational Bayesian Neural Networks
Shengyang Sun*, Guodong Zhang*, Jiaxin Shi*, Roger Grosse.
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi, Shengyang Sun, and Jun Zhu.
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song*, Sahaj Garg*, Jiaxin Shi, Stefano Ermon.
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang.
I created and lead the development of ZhuSuan, a probabilistic programming library with a particular focus on Bayesian deep learning.
My CV can be downloaded from this link: [pdf]