Meng Zhang, researcher
Meng Zhang

Meng Zhang

Assistant Professor@ZJU-UIUC

Haining, Zhejiang
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mengzhang [at] intl.zju.edu.cn※

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Biography

Meng Zhang is an Assistant Professor at the ZJU-UIUC Institute, Zhejiang University. His research interests include wireless and computer networks, optimization for intelligent networks, edge intelligence, and decentralized machine learning.

Meng Zhang received his Ph.D. from the Chinese University of Hong Kong in 2019 and his B.Eng. from South China University of Technology. He conducted postdoctoral research at Northwestern University and was a visiting student research collaborator at Princeton University during his doctoral studies.

Keywords of Research

Wireless and computer networks|optimization for intelligent networks|edge intelligence|decentralized machine learning

Latest News

  1. Our paper "Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing" is accepted by IEEE/ACM Transactions on Networking

    We are glad to share that our paper "Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing" has been accepted by the IEEE/ACM Transactions on Networking (ToN).The paper develops asynchronous, fractional multi-agent deep reinforcement learning to minimize the age of information in mobile edge computing, coordinating multiple agents that make decisions

  2. Our paper "When Sample Selection Bias Precipitates Model Collapse" is accepted by ICML 2026

    We are glad to share that our paper "When Sample Selection Bias Precipitates Model Collapse" has been accepted to the International Conference on Machine Learning (ICML 2026).The paper analyzes how sample selection bias can precipitate model collapse — the progressive quality loss that can occur when models are trained on selectively filtered or model-generated data — identifying the conditions un

  3. Our paper "Timely CPU Scheduling for Computation-intensive Status Updates" is accepted by IEEE Transactions on Information Theory

    We are glad to share that our paper "Timely CPU Scheduling for Computation-intensive Status Updates" has been accepted by the IEEE Transactions on Information Theory (TIT).The paper develops CPU scheduling policies that keep computation-intensive status updates fresh, characterizing and minimizing the age of information when each update requires non-trivial computation before it can be delivered.

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Courses

Meng Zhang teaches courses related to wireless and computer networks, intelligent network optimization, and edge intelligence at the ZJU-UIUC Institute.

Nexus Lab

Meng Zhang leads the Nexus Lab at Zhejiang University, focusing on research in network economics, age of information, mechanism design, and related areas.

Professional Activities

  • Reviewer for top journals and conferences, including
    • IEEE/ACM Transactions on Networking (IEEE/ACM ToN)
    • IEEE Transactions on Mobile Computing (IEEE TMC)
    • IEEE Journal on Selected Areas in Communications (IEEE JSAC)
    • IEEE Transactions on Wireless Communications (IEEE TWC)
    • IEEE INFOCOM
    • WiOpt.

Recent Publications

  • L. Jin, Y. Zhang, Y. Li, S. Wang, H. H. Yang, J. Wu, and M. Zhang*, MoE²: Optimizing Collaborative Inference for Edge Large Language Models, IEEE/ACM Transactions on Networking (ToN), 2026.

  • M. Tang, L. Jin, M. Zhang*, and H. Wang, Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing, To appear in IEEE/ACM Transactions on Networking (ToN), 2026.

  • J. Zhu, C. Feng, G. Geraci, C. Liu, M. Zhang, and H. H. Yang, The Meta Distribution of the SINR in Joint Communication and Sensing Networks, To appear in IEEE Transactions on Network Science and Engineering (TNSE), 2026.

  • M. Zhou, M. Zhang*, H. H. Yang, and R. Yates, Timely CPU Scheduling for Computation-intensive Status Updates, To appear in IEEE Transactions on Information Theory (TIT), 2026.

  • N. Yang, Y. Liu, S. Chen, M. Zhang, and H. Zhang, Minimizing Age of Information in Mobile Edge Computing: Nested Index Policy with Preemptive and Non-Preemptive Structure, IEEE Transactions on Mobile Computing (TMC), 2026.

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