ZHANG, Liang
POSITION/TITLE
Research Scientist
RESEARCH FIELD
Reinforcement learning, Green building, Game theory & network optimization
zhangliang@sribd.cn
EDUCATION BACKGROUND
9/2011-9/2016 PhD in Department of Computing, The Hong Kong Polytechnic University
9/2007-6/2011 Bachelor degree, Huazhong University of Science and Technology
BIOGRAPHY
Dr. ZHANG Liang is research scientist in SRIBD. Before that, he is an associate researcher in Peng Cheng Laboratory and selected as Shenzhen Peacock Program C talent. Dr ZHANG graduated from The Hong Kong Polytechnic University in 2016; from 2017 to 2022, he joined JD.com and Tencent, and published a number of reinforcement learning decision-making research papers such as KDD and SIGIR, which were successfully applied in commercial advertising in JD and The Honor of King in Tencent. He has published more than 20 papers with 1100+ Google citations. His research interests include reinforcement learning and its applications, green buildings, and network optimization.
ACADEMIC PUBLICATIONS
Network optimization
1,Y. Zhao, H. Wang, H. Su, L. Zhang, R. Zhang, D. Wang, K. Xu,“Understand love of variety in wireless data market under sponsored data plans”,IEEE JSAC 2020 (CCF-A)
2,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. (CCF-B)
3, Liang Zhang, Weijie Wu and Dan Wang, "TDS: Time-Dependent Sponsored Data Plan for Wireless Data Traffic Market", in Proc. of IEEE INFOCOM 2016. (CCF-A)
4, Liang Zhang, Weijie Wu and Dan Wang,"Sponsored Data Plan: A Two-Class Service Model in Wireless Data Networks", in Proc. of ACM SIGMETRICS 2015. (CCF-B, CORE* A)
5, Liang Zhang, Weijie Wu and Dan Wang, "Time Dependent Pricing in Wireless Data Networks: Flat-rates vs. Usage-based Schemes", in Proc. of IEEE INFOCOM, 2014 (CCF-A)
Green Building
6, Z Zheng, F Wang, D Wang, L Zhang, "An Urban Mobility Model with Buildings Involved:
Bridging Theory to Practice", ACM TOSN 2020 (CCF-B)
7,Z. Zheng, F. Wang, D. Wang, and L. Zhang, "Buildings affect Mobile Pattens: Developing a new Urban Mobility Model", in Proc. of ACM Buildsys’18 (Best Paper Award)
8,L. Zhang, A. H. Lam and D. Wang, "Strategy proof Thermal Comfort Voting in Buildings,
in Proc. of ACM BuildSys’14
Reinforcement Learning and its applications
9, 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 (CCF-A)
10, 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 (CCF-A)
11, 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 (CCF-B)
12, 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 (CCF-B，google scholar 270+)
13, 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. (CCF-A google scholar 280+)
14,W. Lu, F. Chung, K. Lai, L. Zhang,"Recommender system based on scarce information mining", Neural Networks, 2017 (CCF-B)
15, Q Dai, X Shen, Z Zheng, L Zhang, Q Li, D Wang, "Adversarial training regularization for negative sampling based network embedding", Information Sciences 2021 (CCF-B)
16, Q. Dai, X. Shen, L. Zhang, Q. Li, D. Wang, "Adversarial Training Methods for Network Embedding", in Proc. of ACM WWW 2019 (CCF-A)
