I am a postdoctoral researcher at Microsoft Research New England. My research interests are in probabilistic methods for machine learning. I have worked on topics including approximate inference, Gaussian processes, kernel/spectral methods, generative models, and Bayesian neural networks.

I obtained my Ph.D. in Computer Science (2015-2020) from Tsinghua University, advised by Jun Zhu. 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. I received my B.E. from the Department of Computer Science and Technology at Tsinghua University.

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Research Highlights


Nonparametric Score Estimators

Yuhao Zhou, Jiaxin Shi, and Jun Zhu.

ICML, 2020. [pdf] [arxiv] [code]

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]


During my PhD I led the development of ZhuSuan [github] [doc] [arxiv], an open-source probabilistic programming project based on Tensorflow.

Curriculum Vitae

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