B.Eng. (Yingcai Honors College, University of Electronic Science and Technology of China)

Ph.D and Postdoc (Information Engineering Department, The Chinese University of Hong Kong)

Research Scientist (École polytechnique fédérale de Lausanne)

Dr. Tang is an Assistant Professor and President's Young Scholar at the School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen). Her primary research interests include federated learning, large models, intelligent optimization and game theory for electric vehicle charging networks, and trustworthy artificial intelligence. In recent years, she has published more than 70 academic papers in international journals and conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, AAAI, AISTATS, EMNLP, IEEE SmartGridComm, TMC, TII, TSG, TPWRS, TMLR, and IOTJ. She is the sole first-author recipient of the Best Paper Award for IEEE SmartGridComm 2013, and has received the IEEE/ACM ASE 2023 Distinguished Paper Award, IEEE SmartGridComm 2024 Best Paper Award, and IEEE ICCT 2024 Best Poster Presentation Award. Currently, she serves as an Associate Editor for the journal IEEE IOTJ (JCR Q1). Dr. Tang has been selected for the 2024 Tencent Rhino Bird Special Research Program and Guangdong Province's Young Talent Program. She is also a committee member of IEEE PES China Electric Vehicle Technology Committee and a standing committee member of the Guangdong Computer Society's Mobile and Edge Computing Specialized Committee, as well as a council member of the Shenzhen Artificial Intelligence Society.

Book:

W. Tang and Y. J. Zhang
Optimal Charging Control of Electric Vehicles in Smart Grids
Springer Briefs (Series Editor: Xuemin Sherman Shen), Springer, 2017 (ISBN: 9783319458618, Page Number: XI, 106)

Journal & Conference Papers (selected):

1) Federated Learning

1. L. Wang#, Y. Guo#, T. Lin, and X. Tang*, “DELTA: Diverse Client Sampling for Fasting Federated Learning,” Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, Dec. 2023. (CCF A)
2. Y. Guo#, X. Tang* and T. Lin, “FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering,” International Conference on Machine Learning (ICML), 2024. (CCF A)
3. Y. Guo#, X. Tang* and T. Lin, “FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction,” International Conference on Machine Learning (ICML), 2023. (CCF A)
4. Y. Guo#, X. Tang∗, and T. Lin, “Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies,” The International Conference on Learning Representations (ICLR), 2025.
5. Z. Pan#, C. Li, F. Yu, S. Wang, H. Wang, X. Tang * and J. Zhao*, “FedLF: Layer-Wise Fair Federated Learning,” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024. (CCF A)
6. Z. Pan#, S. Wang, C. Li, H. Wang, X. Tang * and J. Zhao*, “FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance,” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023. (CCF A)
7. Z. Pan#, Z. Wang, C. Li, K. Zheng, B. Wang, X. Tang∗, and J. Zhao∗, “Federated Unlearning with Gradient Descent and Conflict Mitigation,” accepted by The 39th Annual AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF A)
8. Z. Wang#, L. Wang#, Y. Guo#, Y. J. Zhang, and X. Tang∗, “FedMABA: Towards Fair and Resource-Friendly Federated Learning through Multi-Armed Bandits Allocation,” IEEE 24th International Conference on Communication Technology (ICCT), 2024. (Excellent poster presentation Award)
9. Y. Guo#, L. Wang#, X. Tang*, and T. Lin, "Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing, " accepted by International Conference on Computer Vision (ICCV), 2025. (CCF A)

2) LLM（reasoning, MOE, multimodal）

1. Y. Guo#, Z. Cheng, X. Tang∗, Z. Tu, and Tao Lin*, “Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models,” accepted by The International Conference on Learning Representations (ICLR), 2025. (Top 3 ML conference).
2. Y. Guo#, J. Liu, M. Li, Q. Liu, X. Chen, and X. Tang∗, “TRACE: Temporal Grounding Video LLM via Causal Event Modeling,” accepted by The International Conference on Learning Representations (ICLR), 2025. (Top 3 ML conference).
3. Y. Guo#, J. Liu, M. Li, D. Cheng, X. Tang∗, D. Sui, Q. Liu, X. Chen, and K. Zhao, “VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding,” accepted by The 39th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024. (Selected as Highlights) (CCF-A)
4. B. Pan, Q. Li, X. Tang, W. Huang, Z. Fang, F. Liu, J. Wang, J. Yu, and Y. Shi, “NLPrompt: Noise-Label Prompt Learning for Vision-Language Models,” accepted by CVPR, 2025. (CCF-A, selected as Highlights)
5. S. Zhao#, Y. Yuan, X. Tang, and P. He, “Difficult Task Yes but Simple Task No: Unveiling the Laziness in Multimodal LLMs,” the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), Miami, Florida, 2024. (CCF B)

3) Electric vehicle charging network optimization: intelligent optimization and game analysis

1. H. Yan#, C. Sun#, H. Liao#, and X. Tang*, ``Optimal pricing and charging strategy design for non-cooperative battery swapping stations,” IEEE Transactions on Mobile Computing, Vol. 23, no. 12, Dec. 2024.  (中科院1区, CCF A)
2. H. Yan# and X. Tang*, ``Incorporating Bounded Rationality into Electric Vehicle Highway Charging Decisions: A Bayesian Game Analysis,” accepted by IEEE Internet of Things Journal, 2025.  (JCR-Q1)
3. H. Yan#, C. Sun#, H. Liao#, and X. Tang*, “Real-time Optimal Charging Strategy for Battery Swapping Stations under Time-of-use Pricing,” accepted by IEEE Transactions on Network Science and Engineering, 2025.  (JCR-Q1)
4. J. Liu#, Y. Guo#, X. Leng#, and X. Tang*, “Personalized Federated Management and Load Balancing for Multiple Charging Stations”, IEEE Transaction on Industrial Informatics, accepted, Apr. 2025. (中科院1区Top)
5. J. Liu#, S. Wang, and X. Tang*, ``Cooperative Charging Stations Management under Irrational Hierarchy EV Behaviors,'' IEEE Internet of Things Journal, vol. 11, no. 6, Mar. 2024. (JCR-Q1)
6. J. Liu#, J. Zhu#, X. Guan, Y. Luo, and X. Tang, Hierarchical Energy Management and Charging Scheduling in the PV-CS-EV Integrated System, accepted by IEEE Internet of Things Journal, 2025.  (JCR-Q1)
7. J. Zhu#, J. Liu#, and X. Tang*, “A Hybrid Photovoltaic Generation Forecasting Framework Based on Ensemble Learning and Multi-Strategy Improved Optimizer,” accepted by Computers and Industrial Engineering, 2025. (JCR-Q1)
8. C. Sun#, T. Li, and X. Tang*, ``A data-driven approach for optimizing early-stage electric vehicle charging station placement,'' IEEE Transactions on Industrial Informatics, vol. 20, no. 10, Oct. 2024. (中科院1区Top)
9. C. Sun#, X. Tang, and J. Huang, “Strategic V2G Trading in Local Energy Market Considering Minimum Energy Requirement,” IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Oslo, Norway, 2024. (Best Paper Award)
10. H. Liao#, C. Yang, H. Gao, W. Liu, H. Xin, X. Tang*, and J. Zhao*, “Comprehensive-Contribution-Based Primary Frequency Regulation Market Design for the Converter-Integrated Power System,” IEEE Transactions on Power Systems, vol. 39, no. 2, Mar. 2024. (中科院1区Top)
11. J. Zhang#, C. Sun, and X. Tang*, “Incorporating Credit Mechanism for Joint Pricing and Charging Optimization for an Electric Taxi Charging System,” accepted by IEEE Transactions on Vehicular Technology, 2025.  (JCR-Q1)