Tsinghua SAIL Group

Statistical Artificial Intelligence & Learning

Research Overview

We are interested in developing machine learning theories, algorithms, and applications to problems in science, engineering and computing. We use the tools of statistical inference and large-scale computing to deal with uncertainty and information in various domains, including text mining, image & video processing, network analysis, and neuroscience.
Our recent projects include deep learning, scalable (regularized) Bayesian inference, topic models, adversarial examples, reinforcement learning, interpretable and robust machine learning, and their applications in various domains.
We actively seek to collaborate with other groups around the world. If you are interested in finding out more about our research, please visit our publication page.


  • Looking for highly motivated post-docs to work on large-scale machine learning, deep learning, and/or applications in image, text, and network analysis.
  • 2016/1: Jun Zhu got support from "National High-Level Talents Special Support Plan" ("万人计划"青年拔尖人才)
  • 2015/12: Tian Tian was awarded the Tsinghua Special Grade Scholarship for Graduate Students (研究生特等奖学金)
  • 2015/12: Ming Liang was awarded the Baidu Scholarship.
  • 2015/11: Jun Zhu received the CVIC SE Talents Award (中创软件人才奖).
  • 2015/09: Our group has 3 NIPS papers. Congratulations to the authors.
  • 2015/08: Tian Tian was awarded the Siebel Scholarship.