Zixin Wang

Zixin Wang

Post-doctoral fellow
Dept. ECE
HKUST

Hi, I’m Zixin Wang (王子鑫)

Currently, I’m a post-doctoral fellow in the Dept. ECE, Hong Kong University of Science and Technology (HKUST), working with Prof. Khaled B. Letaief.

Prior to this, I have received the Ph.D. degree from University of Chinese Academy of Sciences (ShanghaiTech University), Shanghai, China, in 2024, supervised by Prof. Yong Zhou, and the B.Sc. degree from Wuhan University of Technology, Wuhan, China, in 2018. During Nov. 2022 to Oct. 2023, I was a visiting doctoral researcher in CWC, Oulu university, supervised by Prof. Mehdi Bennis.

My research areas include edge AI, radio resource management, and distributed AI in B5G/6G.

News

  • Sept. 2024, it is glad to attend IEEE Hong Kong 6G Wireless Summit.
  • Sept. 2024, our work “Over-the-air Federated Graph Learning” is accepted by IEEE Trans. Wireless Commun..
  • Aug. 2024, our work “Graph Attention-based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems” is accepted by IEEE Trans. Wireless Commun..
  • Aug. 2024, our work “Federated Low-Rank Adaptation for Large Language Model Fine-Tuning Over Wireless Networks” is accepted by GLOBECOM 2024.
  • Mar. 2024, I joined in HKUST as a post-doctoral fellow, working with Prof. Khaled B. Letaief.
  • Nov. 2023, I passed my thesis defense.
  • Nov. 2022, I start my visiting in Oulu University as a visiting doctoral researcher, working with Prof. Mehdi Bennis.

Highlighted Ongoing Work

  • Edge LLM
    • Our first work in edge LLM, “Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks”, has been submitted to IEEE Trans. Wireless Commun.. Link

Selected First-author Papers

Communication For AI

  • Z. Wang, M. Bennis, and Y. Zhou. ‘‘Graph Attention-based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems’’, accepted for publication in IEEE Trans. Wireless Commun., 2024.
  • Z. Wang, Y. Zou, Q. An, Y. Zhou, Y. Shi, and M. Bennis. ‘‘A Graph Neural Network Learning Approach to Optimize RIS-Assisted Federated Learning’’, accepted for publication in IEEE Trans. Wireless Commun., 2023.
  • Z. Wang, Y. Zhou, and Y. Shi. ‘‘Over-the-air Federated Graph Learning’’, submitted to IEEE Trans. Wireless Commun., 2023 (Minor review).
  • Z. Wang, Y. Zhou, Y. Shi, and K. B. Letaief. ‘‘Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks’’, submitted to IEEE Trans. Wireless Commun., 2024.

AI For Communication

  • Z. Wang, J. Zong, Y. Zhou, Y. Shi, and V. W.S. Wong. ‘‘Decentralized Multi-Agent Power Control in Wireless Networks with Frequency Reuse’’ accepted for publication in IEEE Trans. Commun., 2021.

我见青山多妩媚, 料青山见我应如是。—— 辛弃疾 (1140–1207)