Trustworthy AI Lab @ TsinghuaSIGS

Founded since 2019, the Trustworthy AI Lab is dedicated to improving the robustness, adaptability, and explainability of neural learning systems in the wild. A key focus of our work is trustworthy transfer learning through the study of transferability estimation.

See the Research Page to learn more.

Group Photo

For Perspective MS/PhD Students

You are welcome to apply through the Tsinghua SIGS admission system. If you are emailing me to inquire about your graduate application for the first time, please also include a coding project sample (e.g. github repository link) and an English writing sample (e.g. a scientific paper or a project report).

Requirements

  • Highly motivated students interested in impactful data-centric research

  • CS/EE or STEM background with CS training

  • Bonus: Dedicated research experience in a particular field (quality > quantity)

  • Bonus: Hands on experience of developing efficient AI/software system

Postdoc Position

I'm currently looking for a postdoc researcher to join our group. Potential research topics:

  • Adaptive neural learning systems for dynamic environment and tasks

  • Modular networks and compositonal learning for complex tasks （with applications in medical images and clinical AI）

The ideal candidate should have a Ph.D. background in machine learning (e.g. especially if you are interested in reliable, explainable machine leanring) or a background in medical image processing with hands-on experience of working with large-scale medical data.

Graduated Students

Ph.D.

  • Spring 2024: Taurai Muvunza (Queen Mary University of London)

  • Spring 2023: Yang Tan (Baidu)

Masters

  • Spring 2024: Qiqi Chen (UNIDO), Dexu Kong (Tencent), Zhiyuan (China Telecom Cloud), Shutong Chen (The Trade Desk)

  • Spring 2023: Yongchi Zhu (Baidu), Guanzi Chen (Ph.D. at Australian National University), Wanda Li (ByteDance), Nan Shen, Zirun Li

  • Spring 2022 Jingge Wang (Ph.D at TBSI) ,Yicong Li (Ph.D at Harvard)