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Yang LiuAssociate Professor

Yang Liu is an Associate Researcher and Associate Professor at Institute forAI Industry Research, Tsinghua University. She graduated from Tsinghua University with a B.S. degree in chemical engineering, and from Princeton University with a Ph.D in chemical and biological engineering. Before joining Tsinghua, She was a Principal Researcher and Research Team Lead at WeBank. She also previously worked at Air Products, andDataminr Inc . She holds more than 20 patents and more than 100 patent applications. Her research was published in well-known journals including Nature, AAAI, IJCAI, USENIX,and ACM TIST, and cited for more than3000 times. She co-authored the first book on federated learning,《联邦学习》(Chinese for Federated Learning) and Federated Learning. She also serves as an associate editor for ACM TIST, and a guest editor for IEEE Intelligent Systems and IEEE BigData, and co-chaired multiple workshops at IJCAI and Neurips. Her research work has been recognized with multiple awards, such as CCF Technology Award and AAAI Innovation Award.

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2007-2012 Princeton University Chemical and Biological Engineering Ph.D.

2003-2007 Tsinghua University Chemical Engineering B.S.


2021 till now Institute for AI Industry Research Associate Researcher, Associate Professor

2018-2021 WeBank Principal Researcher, Research Team Lead

2016-2018 Dataminr Inc. Data Scientist

2012-2015 Air Products Senior Research Engineer

Research Fields:

Dr. Liu’s research interests include machine learning, federated learning, privacy-preserving machine learning, multi-agent systems, statistical mechanics etc.. She conducts industrial research for data privacy, data collaboration and data valuation, and builds platforms and incentive mechanisms in an effort to promote cooperation and win-win in industrial applications in healthcare, automatic driving, and intelligent manufacturing etc.


1. Q Yu, Y Liu*, Y Wang, K Xu, J Liu*,Multimodal Federated Learning via Contrastive Representation Ensemble,International Conference on Learning Representations (ICLR), 2023

2. H Takahashi,J Liu, Y Liu*, Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack,The IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 20233. Y Deng, W Chen, J Ren, F Lyu, Y Liu, Y Liu, Y Zhang,TailorFL: Dual-Personalized Federated Learning under System and Data Heterogeneity,Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (Sensys)2022

4. Y Liu*, X Zhang, Y Kang, L Li, T Chen, M Hong, Q Yang*, FedBCD: A communication-efficient collaborative learning framework for distributed features, IEEE Transactions on Signal Processing 70, 4277-4290, 2022

5. T Zou, Y Liu*, Y Kang, W Liu, Y He, Z Yi, Q Yang, YQ Zhang,Defending batch-level label inference and replacement attacks in vertical federated learning, IEEE Transaction on BigData, 2022

6. Z Liu, Y Chen, Y Zhao, H Yu*, Y Liu*, R Bao, J Jiang, Z Nie, Q Xu & Q Yang*. Contribution-Aware Federated Learning for Smart Healthcare, in Proceedings of the 34th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22). (AAAI’22 Innovative Applications of AI Award)

7. Y Liu, T Fan, T Chen, Q Xu, Q Yang, FATE: An industrial grade platform for collaborative learning with data protection, Journal of Machine Learning Research 22.226 (2021): 1-6.

8. Y. Liu*, T. Chen & Q. Yang. Secure Federated Transfer Learning. IEEE Intelligent Systems, vol. 35, no. 4, pp. 70-82, 1 July-Aug. 2020

9. Y. Liu*, A Huang, Y Luo*, H Huang, Y Liu, Y Chen, L Feng, T Chen, H Yu* and Q Yang, FedVision: Visual Object Detection powered by Federated Learning, Thirty-Second Annual Conference on Innovative Applications of Artificial Intelligence (Innovation Award,AAAI-IAAI’20)

10. Q. Yang, Y. Liu, T. Chen & Y. Tong. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 12:1–12:19, 2019

11. K. Cheng&, T. Fan, Y. Jin, Y. Liu*, T. Chen, Q. Yang, SecureBoost: A Lossless Federated Learning Framework, IEEE Intelligent Systems (Best Paper Award),2021

12. C. Zhang, S. Li, J. Xia, W Wang, F Yan, Y. Liu, BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning, USENIX Annual Technical Conference 2020

13. 《联邦学习》. 电子工业出版社,2020

14. Federated Learning. Morgan & Claypool Publishers 2019

15. Fair and Explainable Dynamic Engagement of Crowd Workers,In Proceedings of the 28th International Joint Conference on Artificial Intelligence (Innovation Reward,IJCAI'19)

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