张亮

　　教师简介

　　张亮，副教授，博士生导师。其在工业界包括京东和腾讯的核心商业化团队工作多年，在数据挖掘和人工智能领域有丰富的研究落地经验。在京东集团，主持强化学习在广告与推荐的应用研究落地；在腾讯集团，负责国民级应用王者荣耀游戏的AI-BOT商业化项目，主持设计并开发基于强化学习方法的AI-BOT匹配算法以及在线投放系统，并成功在王者荣耀上线应用部署。其在数据挖掘、人工智能等领域具有多项研究成果，在顶级会议如SIGKDD，ICLR，SIGIR，AAAI，IJCAI，WWW，MM，INFOCOM，SIGMETRICS等，以及国际顶级期刊如TOSN，JSAC等发表20多篇论文。研究成果在buildsys’18荣获best paper；发表在SIGKDD’18以及RecSys’18论文单篇引用量超过400次；总论文引用量超过1600次。担任多个人工智能顶级会议/期刊审稿人，如CIKM，SIGKDD，NeurIPS以及TKDE，TNNLS等。其主持广东省区域联合青年项目以及广东省重点实验室开放基金等多项省部级项目以及作为子课题负责人参与国家重点研发项目。

　　研究兴趣

　　强化学习应用：广告与推荐，游戏AI

　　大模型：RAG以及联邦学习

　　图表征学习：泛化性理论，可解释性，因果学习

　　学习优化：AI方法学习优化问题，并应用于交通网络、计算机网络等领域。

　　联系方式

　　个人主页：https://sites.google.com/view/liangzhang1111/liang-zhang-hk-polyu

　　联系方式：zhangliang27@mail.sysu.edu.cn zhangliangshuxue@gmail.com

　　工作经历

　　中山大学，网络空间安全学院，副教授/博士生导师

　　深圳市大数据研究院，研究科学家

　　鹏城实验室，副研究员

　　腾讯AI平台部

　　京东广告

　　教育经历

　　香港理工大学，计算学，博士

　　华中科技大学，电子与信息工程，本科

　　代表性论文

T. Long, L. Zhang, L. Zhang*, L. Cui, Adversarial Contrastive Graph Augmentation with Counterfactual Regularization, in Proc. of AAAI 2025
L. Zhang#, T. Long#, Y. Liu, L. Zhang, L. Cui, Q. Shi,Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning, in Proc. of SIGKDD 2025.
Kesheng Zhao, Liang Zhang*, Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks, in Proc. of ICLR 2024
Y. Li, L. Zhang*, X. Lan, D. Jiang*, Towards Adaptable Graph Representation Learning: An Adaptive Multi-Graph Contrastive Transformer, in Proc. of ACM MM 2023
X. Han, X. Zhao*, L. Zhang*, W. Wang, TPSCF: Mitigating Action Hysteresis in Traffic Signal Control with Traffic Predictive Reinforcement Learning, in Proc. of SIGKDD 2023
Y. Su, L. Zhang*, Q. Dai, B. Zhang, J. Yan, S. Xu, D. Wang, Y. He, Y. Bao, and W. Yan, "An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration", in Proc. of IJCAI 2020
D. Zhao, L. Zhang*, B. Zhang, L. Zheng, Y. Bao, W. Yan, "MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations", in Proc. of ACM SIGIR 2020
Y. Wang#, L. Zhang#, Q. Dai, F. Sun, B. Zhang, Y. He, Y. Bao and W. Yan , "Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction", in Proc. of ACM CIKM 2019
Y. Zhao, H, Su, L. Zhang, D. Wang, K. Xu, "Variety Matters: A New Model for the Wireless Data Market under Sponsored Data Plans", in Proc. of IEEE/ACM IWQoS 2019.
Q. Dai, X. Shen, L. Zhang, Q. Li, D. Wang, "Adversarial Training Methods for Network Embedding", in Proc. of ACM WWW 2019
X. Zhao, L. Zhang, L. Xia, Z. Ding, D. Yin, Y. Zhao, J. Tang, "Deep Reinforcement Learning for List-wise Recommendations", in DRL4KDD 2019.
Z. Zheng, F. Wang, D. Wang, and L. Zhang, "Buildings affect Mobile Pattens: Developing a new Urban Mobility Model", in Proc. of ACM Buildsys 2018 (Best Paper Award)
X. Zhao, L. Xia, L. Zhang, Z. Ding, D. Yin, J. Tang, "Deep Reinforcement Learning for Page-wise Recommendations", in Proc. of ACM RecSys 2018
X. Zhao, L. Zhang, Z. Ding, L. Xia, J. Tang, and D. Yin. "Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning". in Proc. of ACM SIGKDD 2018.
L. Zhang, W. Wu and D. Wang, "TDS: Time-Dependent Sponsored Data Plan for Wireless Data Traffic Market", in Proc. of IEEE INFOCOM 2016
L. Zhang, W. Wu and D. Wang, "Sponsored Data Plan: A Two-Class Service Model in Wireless Data Networks", in Proc. of ACM SIGMETRICS 2015
L. Zhang, W. Wu and D. Wang, "Time Dependent Pricing in Wireless Data Networks: Flat-rates vs. Usage-based Schemes", in Proc. of IEEE INFOCOM 2014