I am a PhD student (since 2015) in the Department of Computer Science at Tsinghua University, advised by Jun Zhu. My research interests are in the area of probabilistic methods and approximate inference. I have worked on topics including variational inference, Gaussian processes, kernel/spectral methods, generative models, and Bayesian neural networks.

Last year I spent the summer at DeepMind, London as a research scientist intern and the rest of the year visiting Vector Institute. I have also spent a summer interning at RIKEN-AIP, Tokyo. In 2018, I was awarded the Microsoft Research Asia Fellowship. I received my B.E. from the Department of Computer Science and Technology at Tsinghua University.

Github Twitter

Research Highlights


Sparse Orthogonal Variational Inference for Gaussian Processes

Jiaxin Shi, Michalis K. Titsias, and Andriy Mnih.

AISTATS, 2020. [pdf] [arxiv]

Best Student Paper Runner-Up at AABI 2019.

A Spectral Approach to Gradient Estimation for Implicit Distributions

Jiaxin Shi, Shengyang Sun, and Jun Zhu.

ICML, 2018. [pdf] [arxiv] [code]

Scalable Training of Inference Networks for Gaussian-Process Models

Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.

ICML, 2019. [pdf] [arxiv] [code]

Semi-crowdsourced Clustering with Deep Generative Models

Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang.

NeurIPS, 2018. [pdf] [arxiv] [code]


I’m leading the development of ZhuSuan, a deep probabilistic programming library based on Tensorflow.

ZhuSuan: A Library for Bayesian Deep Learning

Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, and Yuhao Zhou, 2017

[github] [docs] [arxiv]

Curriculum Vitae

My CV can be downloaded from this link: [pdf]