Assistant Professor
Thrust of Data Science and Analytics, The Hong Kong University of Science and Technology (Guangzhou)
Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology
Director of HKUST(GZ) Master of Science in Data-Centric Artificial Intelligence Technology (MSc DCAI)
Director of HKUST(GZ) Center for Blockchain Technology and Digital Media
Email: jingtang@ust.hk
Jing Tang is currently an Assistant Professor of The Hong Kong University of Science and Technology (Guangzhou) (HKUST(GZ)) and The Hong Kong University of Science and Technology (HKUST). He received his Ph.D. degree from the Nanyang Technological University (NTU) in 2018, and his B.Eng. degree from the University of Science and Technology of China (USTC) in 2012. Prior to joining HKUST, he was a Research Assistant Professor at National University of Singapore (NUS).
I am looking for PhD students, Research Assistants and Research Fellows. Please send me your CV if you are interested.
Big Data Management and Analytics
Social Network and Graph Analysis
Machine Learning and LLMs
Blockchains
[KDD '26] Yifan Song, Renchi Yang, and Jing Tang*.
Signed Proximity Matters in Graph-based Recommendation.
Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026.
[KDD '26] Haotian Zhang, Kai Han, Zhizhuo Yin, Shuang Cui, Jing Tang, and Pan Hui.
IMGNN: An Efficient, Effective and Generalizable Algorithm for Influence Maximization in Social Networks.
Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026.
[PVLDB '26] Xiaolong Chen and Jing Tang*.
Augmenting Social Influence of Uncertain Seeds via Probabilistic Link Insertion.
Proceedings of the VLDB Endowment (PVLDB), 2026.
[SIGMOD '26] Runwen Qiu and Jing Tang*.
Efficient Approximate Nearest Neighbor Search via Hemi-Sphere Centroids Graph.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2026.
[SIGMOD '26] Han Linghu, Qianhao Cong, Yuming Huang, Shangqi Lu, Liang Feng, and Jing Tang*.
LLM-Powered Interactive Graph Search: A Scalable and Practical Approach.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2026.
[NeurIPS '25] Yihong Luo, Tianyang Hu, Weijian Luo, Kenji Kawaguchi, and Jing Tang*.
Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation.
Advances in Neural Information Processing Systems (NeurIPS), 2025.
[NeurIPS '25] Yihong Luo, Shuchen Xue, Tianyang Hu, and Jing Tang*.
Noise Consistency Training: A Native Approach for One-step Generator in Learning Additional Controls.
Advances in Neural Information Processing Systems (NeurIPS), 2025.
[EMNLP '25] Wen Tao, Jing Tang*, Alvin Chan, Bryan Hooi, Baolong Bi, Nanyun Peng, Yuansheng Liu, and Yiwei Wang.
How to Make Large Language Models Generate 100% Valid Molecules?
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP Main), 2025.
[EMNLP '25] Xingjian Tao, Yiwei Wang, Yujun Cai, Zhicheng Yang, and Jing Tang*.
Understanding GUI Agent Localization Biases through Logit Sharpness.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
[ICCV '25] Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai, and Jing Tang*.
Learning Few-Step Diffusion Models by Trajectory Distribution Matching.
Proceedings of International Conference on Computer Vision (ICCV), 2025.
[ICCV '25] Yihong Luo, Tianyang Hu, Yifan Song, Jiacheng Sun, Zhenguo Li, and Jing Tang*.
Adding Additional Control to One-Step Diffusion with Joint Distribution Matching.
Proceedings of International Conference on Computer Vision (ICCV), 2025.
[CIKM '25] Qifan Zhang, Xiaobin Hong, Jianheng Tang, Nuo Chen, Yuhan Li, Wenzhong Li, Jing Tang, and Jia Li.
GCoder: Improving Large Language Model for Generalized Graph Reasoning.
Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM), 2025.
[COLM '25] Qifan Zhang, Nuo Chen, Zehua Li, Miao Peng, Jing Tang, and Jia Li.
Improving LLMs' Generalized Reasoning Abilities by Graph Problems.
Proceedings of Conference on Language Modeling (COLM), 2025.
[ACL '25] Chansung Park, Juyong Jiang, Fan Wang, Sayak Paul, and Jing Tang*.
LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL Main), pages 33194–33215, 2025.
[TEVC '25] Boyu Hou, Liang Feng, Xuefeng Chen, Jing Tang, Kay Chen Tan, and Xiaofeng Liao.
Evolutionary Transfer Neural Architecture Search across Spaces via Representation Learning.
IEEE Transactions on Evolutionary Computation (TEVC), 2025.
[TKDE '25] Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang*, Sunghun Kim*, and Jiayi Huang*.
A Survey on Mixture of Experts in Large Language Models.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 37(7):3896–3915, 2025.
[ICLR '25] Fan Wang, Juyong Jiang, Chansung Park, Sunghun Kim*, and Jing Tang*.
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models.
Proceedings of the 12th International Conference on Learning Representations (ICLR), 2025.
[ICLR '25] Yihong Luo, Xiaolong Chen, Xinghua Qu, Tianyang Hu, and Jing Tang*.
You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs.
Proceedings of the 12th International Conference on Learning Representations (ICLR), 2025.
[ICLR '25] Zhicheng Yang, Yiwei Wang, Yinya Huang, Zhijiang Guo, Wei Shi, Xiongwei Han, Liang Feng, Linqi Song, Xiaodan Liang, and Jing Tang*.
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling.
Proceedings of the 12th International Conference on Learning Representations (ICLR), 2025.
[ICLR '25] Yuhan Chen, Yihong Luo, Yifan Song, Pengwen Dai, Jing Tang*, and Xiaochun Cao*.
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs.
Proceedings of the 12th International Conference on Learning Representations (ICLR), 2025.
[WWW '25] Shuzheng Wang, Yue Huang, Wenqin Zhang, Yuming Huang, Xuechao Wang, and Jing Tang*.
Private Order Flows and Builder Bidding Dynamics: The Road to Monopoly in Ethereum's Block Building Market.
Proceedings of the ACM Web Conference (WWW), pages 2144–2157, 2025.
[WWW '25] Shuang Cui, Kai Han, and Jing Tang.
Linear-Time Algorithms for Representative Subset Selection From Data Streams.
Proceedings of the ACM Web Conference (WWW), pages 4710–4721, 2025.
[PVLDB '25] Yifan Song, Xiaolong Chen, Wenqing Lin, Jia Li, Chen Zhang, Yan Zhou, Lei Chen, and Jing Tang*.
Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph.
Proceedings of the VLDB Endowment (PVLDB), 18(4):929–942, 2025.
[KDD '25] Xiaolong Chen and Jing Tang*.
Scalable Link Recommendation for Influence Maximization.
Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 130–141, 2025.
[SIGMETRICS '25] Yuming Huang, Jing Tang*, Qianhao Cong, Richard T. B. Ma, Lei Chen, and Yeow Meng Chee.
The Last Survivor of PoS Pools: Staker's Dilemma.
Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 9(1):11:1–11:29, 2025.
Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pages 160–162, 2025.
[OE '25] Siya Qiu, Yihong Luo, Qiong Luo, and Jing Tang*.
SteadySeg: Improving Maritime Trajectory Staging by Steadiness Recognition.
Ocean Engineering (OE), Volume 318, Article 120136, 2025.
[JAIR '25] Shuang Cui, Kai Han, Jing Tang, Xueying Li, Zhiyu Li, and Hanxiao Li.
Practical Parallel Algorithms for Non-Monotone Submodular Maximization.
Journal of Artificial Intelligence Research (JAIR), 82:39–75, 2025.
[NeurIPS '24] Yihong Luo, Yuhan Chen, Siya Qiu, Yiwei Wang, Chen Zhang, Yan Zhou, Xiaochun Cao*, and Jing Tang*.
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification.
Advances in Neural Information Processing Systems (NeurIPS), pages 132364–132387, 2024.
[NeurIPS '24] Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu.
Optimal Batched Best Arm Identification.
Advances in Neural Information Processing Systems (NeurIPS), pages 134947–134980, 2024.
[NeurIPS '24] Haiming Wang, Huajian Xin, Zhengying Liu, Wenda Li, Yinya Huang, Jianqiao Lu, Zhicheng Yang, Jing Tang, Jian Yin, Zhenguo Li, and Xiaodan Liang.
Proving Theorems Recursively.
Advances in Neural Information Processing Systems (NeurIPS), pages 86720–86748, 2024.
[EMNLP '24] Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, and Jing Tang*.
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2857–2896, 2024.
[ECCV '24] Yihong Luo, Siya Qiu, Xingjian Tao, Yujun Cai, and Jing Tang*.
Energy-Calibrated VAE with Test Time Free Lunch.
Proceedings of the 18th European Conference on Computer Vision (ECCV), pages 326–344, 2024.
[PDF]
[PVLDB '24] Bing Tong, Yan Zhou, Chen Zhang, Jianheng Tang, Jing Tang, Leihong Yang, Qiye Li, Manwu Lin, Zhongxin Bao, Jia Li, and Lei Chen.
Galaxybase: A High Performance Native Distributed Graph Database for HTAP.
Proceedings of the VLDB Endowment (PVLDB), 17(12):3893–3905, 2024.
[PDF]
[WWW '24] Xiaolong Chen, Yifan Song, and Jing Tang*.
Link Recommendation to Augment Influence Diffusion with Provable Guarantees.
Proceedings of the ACM Web Conference (WWW), pages 2509–2518, 2024.
[PDF]
[ICLR '24] Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li, and Xiaodan Liang.
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning.
Proceedings of the 12th International Conference on Learning Representations (ICLR), 2024.
[PDF]
[CoNLL '23] Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, and Muhao Chen.
How Fragile is Relation Extraction under Entity Replacements?
Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 414–423, 2023.
[PDF]
[KDD '23] Shiqi Zhang, Renchi Yang, Jing Tang, Xiaokui Xiao, and Bo Tang.
Efficient Approximation Algorithms for Spanning Centrality.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 3386–3395, 2023.
[PDF]
[IJCAI '23] Yuhan Chen#, Yihong Luo#, Jing Tang*, Liang Yang, Siya Qiu, Chuan Wang*, and Xiaochun Cao.
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity.
Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), pages 3550–3558, 2023.
[PDF]
[WWW '23] He Huang, Kai Han, Shuang Cui, and Jing Tang.
Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular Objectives.
Proceedings of the ACM Web Conference (WWW), pages 3530–3540, 2023.
[PDF]
[WWW '23] Shuang Cui, Kai Han, Jing Tang, and He Huang.
Constrained Subset Selection from Data Streams for Profit Maximization.
Proceedings of the ACM Web Conference (WWW), pages 1822–1831, 2023.
[PDF]
[WWW '23] Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, and Xiaokui Xiao.
Node-wise Diffusion for Scalable Graph Learning.
Proceedings of the ACM Web Conference (WWW), pages 1723–1733, 2023.
[PDF]
[AAAI '23] Shuang Cui, Kai Han, Jing Tang, He Huang, Xueying Li, and Zhiyu Li.
Practical Parallel Algorithms for Submodular Maximization subject to a Knapsack Constraint with Nearly Optimal Adaptivity.
Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), pages 7261–7269, 2023.
[PDF]
[SIGMOD '23] Renchi Yang and Jing Tang*.
Efficient Estimation of Pairwise Effective Resistance.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 1(1):16:1–16:27, 2023.
[PDF]
[TKDE '23] Yuqing Zhu, Jing Tang*, Xueyan Tang, Sibo Wang, and Andrew Lim.
2-hop+ Sampling: Efficient and Effective Influence Estimation.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(2):1088–1103, 2023.
[PDF]
[SIGMETRICS '23 & POMACS '22] Shuang Cui, Kai Han, Jing Tang, He Huang, Xueying Li, and Zhiyu Li.
Streaming Algorithms for Constrained Submodular Maximization.
Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pages 65–66, 2023.
Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 6(3):54:1–54:32, 2022.
[PDF]
[NeurIPS '22] Qing Xiu, Kai Han, Jing Tang, Shuang Cui, and He Huang.
Chromatic Correlation Clustering, Revisited.
Advances in Neural Information Processing Systems (NeurIPS), pages 26147–26159, 2022.
[PDF]
[KDD '22] Qianhao Cong, Jing Tang*, Kai Han, Yuming Huang, Lei Chen, and Yeow Meng Chee.
Noisy Interactive Graph Search.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 231–240, 2022.
[PDF]
[TODS '22] Qintian Guo, Sibo Wang, Zhewei Wei, Wenqing Lin, and Jing Tang.
Influence Maximization Revisited: Efficient Sampling with Bound Tightened.
ACM Transactions on Database Systems (TODS), 47(3):12:1–12:45, 2022.
[PDF]
[VLDBJ '22] Yuqing Zhu, Jing Tang*, and Xueyan Tang.
Optimal Price Profile for Influential Nodes in Online Social Networks.
The VLDB Journal (VLDBJ), 31(4):779–795, 2022.
[PDF]
[ICDE '22] Jing Tang*, Yuqing Zhu, Xueyan Tang, and Kai Han.
Distributed Influence Maximization for Large-Scale Online Social Networks.
Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 81–95, 2022.
[PDF]
[ICDE '22] Qianhao Cong, Jing Tang*, Yuming Huang, Lei Chen, and Yeow Meng Chee.
Cost-Effective Algorithms for Average-Case Interactive Graph Search.
Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 1152–1165, 2022.
[PDF] [arXiv]
[PVLDB '22] Yuqing Zhu, Jing Tang*, Xueyan Tang, and Lei Chen.
Analysis of Influence Contribution in Social Advertising.
Proceedings of the VLDB Endowment (PVLDB), 15(2):348–360, 2022.
[PDF]
[NeurIPS '21] Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer.
Advances in Neural Information Processing Systems (NeurIPS), pages 11096–11107, 2021.
[PDF]
[ICML '21] Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, and Quanquan Gu.
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits.
Proceedings of the International Conference on Machine Learning (ICML), pages 5065–5073, 2021.
[PDF]
[ICML '21] Tianyuan Jin, Keke Huang, Jing Tang, and Xiaokui Xiao.
Optimal Streaming Algorithms for Multi-Armed Bandits.
Proceedings of the International Conference on Machine Learning (ICML), pages 5045–5054, 2021.
[PDF]
[ICML '21] Shuang Cui, Kai Han, Tianshuai Zhu, Jing Tang, Benwei Wu, He Huang.
Randomized Algorithms for Submodular Function Maximization with a k-System Constraint.
Proceedings of the International Conference on Machine Learning (ICML), pages 2222–2232, 2021.
[PDF] [arXiv]
[SIGMOD '21] Kai Han, Benwei Wu, Jing Tang, Shuang Cui, Cigdem Aslay, and Laks V.S. Lakshmanan.
Efficient and Effective Algorithms for Revenue Maximization in Social Advertising.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 671–684, 2021.
[PDF]
[SIGMOD '21] Yuming Huang#, Jing Tang#*, Qianhao Cong, Andrew Lim, and Jianliang Xu.
Do the Rich Get Richer? Fairness Analysis for Blockchain Incentives.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 790–803, 2021.
[PDF] [arXiv]
[SIGMETRICS '21 & POMACS '21] Jing Tang*, Xueyan Tang, Andrew Lim, Kai Han, Chongshou Li, and Junsong Yuan.
Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint.
Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pages 63–64, 2021.
Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 5(1):08:1–08:22, 2021.
[PDF] [Abstract] [arXiv] [Acceptance Rate: 12.1% (38 out of 315)]
[ACM MM '20] Xinke Li, Chongshou Li, Zekun Tong, Andrew Lim, Junsong Yuan, Yuwei Wu, Jing Tang, and Raymond Huang.
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene.
Proceedings of the ACM International Conference on Multimedia (ACM MM), pages 238–246, 2020. (Oral)
[PDF] [arXiv] [Acceptance Rate (Oral): 8.9% (151 out of 1698)]
[PVLDB '20] Yuqing Zhu, Jing Tang*, and Xueyan Tang.
Pricing Influential Nodes in Online Social Networks.
Proceedings of the VLDB Endowment (PVLDB), 13(10):1614–1627, 2020.
[PDF]
[ICDCS '20] Jun Zhao, Jing Tang, Zengxiang Li, Huaxiong Wang, Kwok-Yan Lam, and Kaiping Xue.
An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound.
Proceedings of the 40th IEEE International Conference on Distributed Computing Systems (ICDCS), pages 179–189, 2020.
[PDF] [arXiv] [Acceptance Rate: 18.0% (105 out of 584)]
[VLDBJ '20] Keke Huang#, Jing Tang#*, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, and Andrew Lim.
Efficient Approximation Algorithms for Adaptive Influence Maximization.
The VLDB Journal (VLDBJ), 29(6):1385–1406, 2020.
[PDF] [arXiv]
[ICDE '20] Keke Huang, Jing Tang*, Xiaokui Xiao, Aixin Sun, and Andrew Lim.
Efficient Approximation Algorithms for Adaptive Target Profit Maximization.
Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 649–660, 2020.
[PDF] [arXiv]
[JSAC '19] Jing Tang* and Richard T. B. Ma.
Regulating Monopolistic ISPs without Neutrality.
IEEE Journal on Selected Areas in Communications (JSAC), 37(7):1666–1680, 2019.
[PDF]
[SIGMOD '19] Jing Tang#*, Keke Huang#, Xiaokui Xiao, Laks V.S. Lakshmanan, Xueyan Tang, Aixin Sun, and Andrew Lim.
Efficient Approximation Algorithms for Adaptive Seed Minimization.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 1096–1113, 2019.
[PDF] [arXiv]
[PVLDB '19] Kai Han, Fei Gui, Xiaokui Xiao, Jing Tang, Yuntian He, Zongmai Cao, and He Huang.
Efficient and Effective Algorithms for Clustering Uncertain Graphs.
Proceedings of the VLDB Endowment (PVLDB), 12(6):667–680, 2019.
[PDF]
[PVLDB '18] Kai Han, Keke Huang, Xiaokui Xiao, Jing Tang, Aixin Sun, and Xueyan Tang.
Efficient Algorithms for Adaptive Influence Maximization.
Proceedings of the VLDB Endowment (PVLDB), 11(9):1029–1040, 2018.
[PDF]
[SIGMOD '18] Jing Tang*, Xueyan Tang, Xiaokui Xiao, and Junsong Yuan.
Online Processing Algorithms for Influence Maximization.
Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 991–1005, 2018.
[PDF] [Code]
[INFOCOM '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
Towards Profit Maximization for Online Social Network Providers.
Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), pages 1178–1186, 2018.
[PDF] [arXiv] [Acceptance Rate: 19.2% (309 out of 1606), Best-in-Session Presentation Award]
[TKDE '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
Profit Maximization for Viral Marketing in Online Social Networks: Algorithms and Analysis.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 30(6):1095–1108, 2018.
[PDF] [Code]
[TMM '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
Traffic-Optimized Data Placement for Social Media.
IEEE Transactions on Multimedia (TMM), 20(4):1008–1023, 2018.
[PDF]
[SNAM '18] Jing Tang*, Xueyan Tang, and Junsong Yuan.
An Efficient and Effective Hop-Based Approach for Influence Maximization in Social Networks.
Social Network Analysis and Mining journal (SNAM), 8(1):10:1–10:19, 2018.
[PDF] [Code]
[ASONAM '17] Jing Tang*, Xueyan Tang, and Junsong Yuan.
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach.
Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 64–71, 2017.
[PDF] [arXiv] [Code] [Acceptance Rate: 17.2% (33 out of 192)]
[ICNP '16] Jing Tang*, Xueyan Tang, and Junsong Yuan.
Profit Maximization for Viral Marketing in Online Social Networks.
Proceedings of the 24th IEEE International Conference on Network Protocols (ICNP), pages 1–10, 2016.
[PDF] [Code] [Acceptance Rate: 20.0% (46 out of 229)]
[ICDCS '15] Jing Tang*, Xueyan Tang, and Junsong Yuan.
Optimizing Inter-Server Communication for Online Social Networks.
Proceedings of the 35th IEEE International Conference on Distributed Computing Systems (ICDCS), pages 215–224, 2015.
[PDF] [Acceptance Rate: 12.9% (70 out of 543)]
[ICNP '14] Jing Tang* and Richard T. B. Ma.
Regulating Monopolistic ISPs without Neutrality.
Proceedings of the 22nd IEEE International Conference on Network Protocols (ICNP), pages 374–384, 2014.
[PDF] [Acceptance Rate: 20.0% (32 out of 160), Best Paper Award (1 out of 32)]
CES Innovation Award in Artificial Intelligence, 2025
Gold Medal, International Exhibition of Inventions Geneva, Switzerland, 2025
KDD Outstanding Reviewer, 2025
Finalist for the Singapore NRF Fellowship for Artificial Intelligence, Class of 2019
Best-in-Session Presentation Award: The IEEE International Conference on Computer Communications (INFOCOM), 2018
Best Paper Award: The 22nd IEEE International Conference on Network Protocols (ICNP), 2014
NTU Research Scholarship, 2013-2017
Outstanding Graduate Scholarship: University of Science and Technology of China (USTC), 2012
Tech it forward. The Strait Times, 1 July 2018.
Assistant Professor at The Hong Kong University of Science and Technology (Guangzhou), 2021 to date
Assistant Professor at The Hong Kong University of Science and Technology, 2021 to date
Research Assistant Professor at National University of Singapore, 2018–2021
Research Fellow at Nanyang Technological University, 2017–2018
Research Assistant at National University of Singapore, 2012–2013
Local Arrangements Chair: VLDB 2024
Sponsorship Chair: VLDB 2026
Conference Area Chair:
International Conference on Machine Learning (ICML): 2026
International Conference on Learning Representations (ICLR): 2026
AAAI Conference on Artificial Intelligence (AAAI): 2026
ACL Rolling Review (ARR): 2025
Conference Technical Program Committee (TPC) Member:
ACM SIGMOD International Conference on Management of Data (SIGMOD): 2027
International Conference on Very Large Data Bases (VLDB): 2023, 2025
IEEE International Conference on Data Engineering (ICDE): 2023, 2026
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD): 2022, 2023, 2024, 2025, 2026
International Conference on Machine Learning (ICML): 2022, 2023, 2024, 2025
Advances in Neural Information Processing Systems (NeurIPS): 2022, 2023, 2024, 2025
International Conference on Learning Representations (ICLR): 2024, 2025
ACM Web Conference (WWW): 2023, 2024, 2025, 2026
AAAI Conference on Artificial Intelligence (AAAI): 2021, 2022, 2024
ACM International Conference on Information and Knowledge Management (CIKM): 2023, 2024, 2025
SIAM International Conference on Data Mining (SDM): 2024
International Conference on Database Systems for Advanced Applications (DASFAA): 2019, 2020, 2021, 2022, 2023
IEEE International Conference on Distributed Computing Systems (ICDCS): 2020
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): 2019, 2020, 2021, 2022
Journal Reviewer: The VLDB Journal, IEEE TPAMI, IEEE/ACM ToN, IEEE JSAC, IEEE TKDE, IEEE TPDS, IEEE TNNLS, IEEE TCC, IEEE TNSE, IEEE TETC, IEEE TCSS, IEEE TBD, ACM TKDD, Elsevier Information Sciences, Elsevier FGCS, Elsevier JNCA, Elsevier Neurocomputing, Springer JCST, Springer WWW Journal, The Computer Journal, etc.