Portrait of Shuxin Zheng

Shuxin Zheng

Principal Researcher


Dr. Shuxin Zheng (郑书新), a Principal Researcher at Microsoft Research, serves as an Industrial Adjunct Lecturer at Chinese Academy of Sciences and is the Lead for Microsoft’s foundational science models. His research interests encompass general AI and generative AI, along with their applications in the scientific domain. Under his leadership, his team has repeatedly won international competitions focused on AI for Science, and he has published over 20 papers in top-tier scientific journals and AI conferences like Nature Computational Science, accumulating over 2000 citations. Dr. Zheng regularly serves as a reviewer for premier AI conferences and journals, and teaches general courses “Foundations of Machine Learning Methods and Applications” and “Advanced Machine Learning” in Tsinghua University, Chinese Academy of Sciences, and Microsoft AI School.

His representative works include:

  • Graphormer [arxiv (opens in new tab)] [github (opens in new tab)] [blog (opens in new tab)]: the first general-purpose Transformer for graph data, which won the 1st place of KDD Cup 2021 OGB-LSC challenge [kdd (opens in new tab)] [technical report (opens in new tab)] and the 1st Open Catalyst Challenge [link (opens in new tab)], outperforming teams from Google DeepMind, Meta AI Research, and others.
  • General Graphormer (GeG): the world’s largest foundation model (with 22 billion parameters) for molecular science, which can perform multiple scientific tasks across different domains and scales, and serve as a powerful scientific assistant for accelerating scientific discovery.
  • Distributional Graphormer (DiG) [demo (opens in new tab)] [arxiv (opens in new tab)] [blog (opens in new tab)]: a breakthrough model that goes beyond AlphaFold2 by predicting the equilibrium distribution of protein structures, rather than a single structure. DiG is also a disruptive innovation in statistical mechanics, where it uses generative AI technology to revolutionize traditional molecular dynamics simulation or sampling methods.
  • DeepRSM [paper (opens in new tab)]: a joint work with the School of Environment of Tsinghua University, which developed an AI numerical model for regional air quality and climate modeling, and was adopted by China’s “14th Five-Year Plan” as the core technology for controlling air pollution and carbon emission in China.

郑书新博士,微软研究院首席研究员,中科院企业导师,微软科学基础模型负责人。他的研究兴趣涵盖通用AI与生成式AI,以及它们在科学领域的应用。他带领团队多次在科学智能(AI for Science)主题的国际竞赛中夺冠,并在《自然》大子刊等顶级科学期刊或国际人工智能会议上发表20余篇一作或通讯论文,学术引用超过2000次。郑书新研究员长期担任国际顶级AI会议与期刊审稿人,并在清华大学、中科院和微软人工智能学院等兼职讲授《机器学习方法与应用基础》和《高等机器学习》等课程。

I’m looking for highly-motivated collaborators all the time. Please contact me if you’re interested.