Liang Zhang (SRIBD)

Liang Zhang (张亮)

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Liang Zhang is now working as a research scientist at SRIBD. Before that, he was a senior research scientist at Tencent and a Doctoral Management Trainee (DMT) in JD.com. He obtained his PhD from Department of Computing, The Hong Kong Polytechnic University, supervised by Prof. Dan Wang. Before joining PolyU, he received his Bachelor's degree in Department of Electronics and Information Engineering, Huazhong University of Science and Technology, supervised by Prof. Hongbo Jiang.

Research interests:

• Graph Representation Learning

• Recommender/advertising systems

• Reinforcement learning

• Dynamic Pricing and incentive design

Selected Publications:

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(co-first author), 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, A. H. Lam and D. Wang, "Strategy-proof Thermal Comfort Voting in Buildings", in Proc. of ACM BuildSys 2014

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

L. Zhang and D. Wang, "Sponsoring Content: Motivation and Pitfalls for Content Service Providers", in INFOCOM Workshop on Smart Data Pricing 2014 (SDP'14)

L. Zhang, W. Wu, D. Wang, "The Effectiveness of Time Dependent Pricing in Controlling Usage Incentives in Wireless Data Network", Sigcomm 2013, poster

L. Zhang, Q. Ye, J. Cheng, H. Jiang, Y. Wang, R. Zhou, and P. Zhao, "Fault-tolerant Scheduling for Data Collection in Wireless Sensor Networks", in Proc. of IEEE GLOBECOM, 2012.

Activities:

Sigcomm helper in 2013.