研究领域
研究兴趣包括：通信网络资源优化、共享经济、强化学习、机器学习。近期工作主要集中在：
1、分布式学习与优化(联邦学习)
2、基于图神经网络的交通行为预测
3、基于强化学习的交通信号灯控制
工作内容是针对以上问题，设计有效的算法，进行理论分析和仿真验证。
近期论文
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（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）
PRGLight: A novel traffic light control framework with Pressure-based-Reinforcement Learning and Graph Neural Network.[会议录]:IJCAI 2021 Reinforcement Learning for Intelligent Transportation Systems (RL4ITS) Workshop,2021
On the Performance of Multi-message Algebraic Gossip Algorithms in Dynamic Random Geometric Graphs.[期刊]:IEEE Communication Letter,2018
Resource Allocation for Device-to-Device Communications As an Underlay Using Nash Bargaining Game Theory.[会议录]:IEEE ICTC2015,2015
On the Network Sharing of Mixed Network Coding and Routing Data Flows in Congestion Networks.[期刊]:IEEE Trans. On Vehicular Technology,2014
The novel generating algorithm and properties of hybrid-P-ary generalized bridge functions.[期刊]:Science in China: Series F Information Sciences,2005
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