TANG, Xiaoying

Assistant Professor

Presidential Young Fellow
Education Background

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

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

Postdoc/Research Scientist (The Chinese University of Hong Kong & École polytechnique fédérale de Lausanne)

Research Field
Online scheduling and distributed algorithm design and optimizations for smart grid, edge computing and other cyber-physical systems; Fundamental research in machine learning algorithm and artificial intelligence
Academic Area
Computer Engineering, New Energy Science and Engineering, Artificial Intelligence and Robotics
Email
tangxiaoying@cuhk.edu.cn
Biography

Technical Program Committee Co-Chair

IEEE International Conference on Communications (ICC) 2019 Workshop-ICT4SG - 2019

Technical Program Committee Member

IEEE Global Communications Conference (GLOBECOM) - 2019

IEEE Power Systems Computation Conference (PSCC）- 2018

IEEE Asia-Pacific Conference on Communications (APCC) - 2017-2019

IEEE International Conference on Smart Grid Communications (SmartGridComm) - 2015-2016

International Conference on Mechanics & Applied Physics (ICMAPH)) - 2015

 

Currently, Prof. TANG's team has 8 Ph.D. students, 3 postgraduate students, and several undergraduate interns. All the Ph.D. students obtained their bachelor's degree from top 985&211 universities, such as Naikai University, UESTC, Shanghai Jiao Tong University, Beijing University of Aeronautics and Astronautics. Dalian University of Technology, Northwestern Polytechnical University, Central South University.

 

 

Academic Publications

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. L. Wang, Y. Guo, T. Lin, and X. Tang*, “DELTA: Diverse Client Sampling for Fasting Federated Learning”, NeurIPS 2023, New Orleans Ernest N. Morial Convention Center, USA.
  2. Y. Guo, X. Tang* and T. Lin, “FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction”, ICML 2023, Hawaii, USA.
  3. Z. Pan, S. Wang, H. Wang, C. Li, and X. Tang*, J. Zhao*，“FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance”, AAAI 2023, Washington, DC, USA.
  4. L. Wang, Z. Wang, X. Tang*, “FedEBA+: Towards Fair and Effective Federated Learning via Entropy-based Model”, ICLR 2023 Workshop ML4IoT, Kigali, Rwanda.
  5. Y. Guo, X. Tang*, and T. Lin, “FedConceptEM: Robust Federated Learning Under Diverse Distribution Shifts”, ICLR 2023 Workshop ML4IoT, Kigali, Rwanda.
  6. H. Yan and X. Tang*, “Incorporating range anxiety into electric vehicle highway charging decisions: A bayesian game analysis,” in Proceedings of the 14th ACM International Conference on Future Energy Systems, ser. e-Energy'23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 184–188.
  7. 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, pp. 1–11, 2023.
  8. 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, 2023.