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.
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
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.Y. Liu*, T. Chen & Q. Yang. Secure Federated Transfer Learning. IEEE Intelligent Systems, vol. 35, no. 4, pp. 70-82, 1 July-Aug. 2020
2.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)
3.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
4.K. Cheng&, T. Fan, Y. Jin, Y. Liu*, T. Chen, Q. Yang, SecureBoost: A Lossless Federated Learning Framework, IEEE Intelligent Systems，2021
5.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
6. Federated Learning. Publishing House of Electronics Industry，2020
7.Federated Learning. Morgan & Claypool Publishers 2019
8.Fair and Explainable Dynamic Engagement of Crowd Workers，In Proceedings of the 28th International Joint Conference on Artificial Intelligence (Innovation Reward，IJCAI'19)
9.A Fairness-aware Incentive Scheme for Federated Learning, Third AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, NY, USA, 2020
10.Metastable liquid-liquid transition in a molecular model of water. Nature, vol. 510, no. 7505, pp. 385–388, 2014.
11.Low-temperature fluid-phase behavior of ST2 water, The Journal of Chemical Physics 131 (10), 104508