Yang Liu is currently an Associate Professor and Ph.D. Supervisor at Global Institute of Future Technology, Shanghai Jiao Tong University, and is recognized as an Overseas Talent of Beijing City. He received his Ph.D. degree in Electrical Engineering from Michigan Technological University, followed by postdoctoral research at Carnegie Mellon University and Duke University, respectively. He has previously joined JD.com as a member of the Tech Genius Team (TGT), and promoted as a Tech Director of JD Logistics. He has been leading the R&D and application of artificial intelligence technologies in green logistics and supply chain, driving innovations in warehouse automation, inventory optimization, and energy management, thereby creating significant economic value to the logistics industry. In the field of renewable energy, he focuses on the development of AI algorithms for accelerating battery R&D and their applications in the transportation and logistics industries. He has authored over 50 papers (34 as first/corresponding author) in top-tier journals and conferences such as Joule and KDD, holds more than 30 national and international patents, and has contributed to 4 academic monographs. His research outcomes have been applied in companies such as JD.com, Foxconn, and CATL. He has been honored with multiple Prizes from the Postal Industry and the China Federation of Logistics and Purchasing. He has also received the IEEE Systems Journal Best Paper Award.

2007-2011，Huazhong University of Science and Technology，Department of Electronics and Information Engineering，B.S

2011-2016，Michigan Technological University, USA ，Department of Electrical and Computer Engineering，Ph.D

2026-Pres.，Global Institute of Future Technology, SJTU，Tenure-track Associate Professor

2018-2026，JD Logistics ，Tech Director (Selected into the Tech Genius Team of JD)

2017-2018，Duke University，Postdoctoral Research Associate

2016-2017，Carnegie Mellon University ， Postdoctoral Research Associate

2021，Overseas Talent of Beijing City

2021，China Federation of Logistics and Purchasing 2nd Award on Development of Science and Technology

2020，China Federation of Logistics and Purchasing 1st Award on Development of Science and Technology

2020，Postal Service Industry 1st Award on Science and Technology

2018，IEEE Systems Journal Best Paper Award (Top 1%)

Guest Editor: IEEE Transactions on Industrial Informatics

Youth Editorial Committee: Chain

1. Yi Zhong, Yan Leng, Zhi Gu, Shujing Guo, Peiyi Li, Soham Das, Yankehao Liu, Jiayu Wan, and Yang Liu*,“Breaking Interdisciplinary Barriers in Solid-State Battery Research: The BatteryAgent for Multifaceted Analysis”, Journal of Materials Chemistry A, no. 43, pp. 37031-37043, 2025.

2. Xiaoang Zhai, Guohua Liu, Ting Lu, Yang Liu*, Jiayu Wan, and Xin Li, “Leveraging Multi-View Imputation Strategy for Robust Battery Lifetime Prediction under Missing-Data Scenarios”, Energy Storage Materials, vol. 79, p. 104352, 2025.

3. Xiaoang Zhai, Guohua Liu, Ting Lu, Sihui Chen, Yang Liu*, Jiayu Wan, and Xin Li, “Transforming Waste to Value: Enhancing Battery Lifetime Prediction Using Incomplete Data Samples”, Journal of Energy Chemistry, vol. 106, pp. 642-649, 2025.

4. Nanlin Guo, Sihui Chen, Jun Tao, Yang Liu*, Jiayu Wan, and Xin Li, “Semi-Supervised Learning for Explainable Few-Shot Battery Lifetime Prediction”, Joule, vol. 8, no. 6, pp. 1820-1836, 2024.

5. Tianze Lin, Sihui Chen, Stephen Harris, Tianshou Zhao, Yang Liu*, and Jiayu Wan, “Investigating Explainable Transfer Learning for Battery Lifetime Prediction under State Transitions”, eScience, vol. 4, no. 5, p. 100280, 2024.

6. Kun Liu, Peihan Wu, Tao Xia, Yang Liu*, Mingjie Guo, Wenming Zhe, and Yan Cheng, “Fusion-Based Rear License Plate Detection and Recognition Considering Enlarged Prints”, IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11, 2024.

7. Shiqi Hao, Yang Liu*, Yu Wang, Xiaopeng Huang, Muchuan Zhao, and Xiaotian Zhuang, “Catalyzing Intelligent Logistics System Simulation with Data-Driven Decision Strategies”, Winter Simulation Conference, pp. 632-643, 2024.

8. Yang Liu, Xin Tao, Xin Li, Armando Colombo, and Shiyan Hu, “Artificial Intelligence in Smart Logistics Cyber-Physical Systems: State-of-The-Arts and Potential Applications”, IEEE Transactions on Industrial Cyber-Physical Systems, vol. 1, pp. 1-20, 2023.

9. Botong Liu, Yang Liu*, Shiyan Hu, and Wenming Zhe, “Opportunities and Challenges of Scheduling in Logistics Industrial Park Cyber-Physical Systems”, IEEE Transactions on Industrial Cyber-Physical Systems, vol. 1, pp. 322-334, 2023.

10. Shiqi Hao, Yang Liu*, Yu Wang, Yuan Wang, and Wenming Zhe, “Three-Stage Root Cause Analysis for Logistics Time Efficiency via Explainable Machine Learning”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2987-2996, 2022.

Deep Reinforcement Learning and Design Methods of Decision Systems (English)

Statistical Signal Processing (English)