Skip to content
Eng · 繁體 · 简体

 [   ] 

Prof. YANG Yu (楊禹博士)

BEng(HFUT), MEng(USTC), PhD(SFU)

Assistant Professor

Contact Information

Office:  LAU-16-283
Phone: (+852) 3442-4035
Email: yuyang@cityu.edu.hk
Web: Personal Homepage

Research Interests

  • Algorithmic Data Science
  • Mining data of combinatorial structures
  • Data-Driven Operations Management
  • AI Empowered Medical Wearables
Yu Yang obtained his Ph.D. in Computing Science from Simon Fraser University in Feb. 2019. Before that, he obtained his M.E. from University of Science and Technology of China in 2013, and his B.E. from Hefei University of Technology in 2010, both in Computer Science.

His research interests lie in the algorithmic aspects of data science, with an emphasis on devising effective and efficient algorithmic tools for mining data of combinatorial structures (such as graphs, sets and sequences) and data-driven operations management. He also has strong interests in machine learning theory, especially in applying learning theory to accelerate data processing.


Awards and Achievements

  • 2019 “Governor General's Gold Medal” The Governor General of Canada.
  • 2022 “Silver Award” Geneva International Exhibition of Inventions.
  • 2025 “SDM 2025 Best Applied Data Science Paper Award” Society for Industrial and Applied Mathematics.


Publications Show All Publications Show Prominent Publications


Journal

  • Zhu, Lyuyi. , Zhang, Qixin. , Jian, Xiangru. & Yang, Yu. (Jul 2025). Graph convolutional network for traffic incidents duration classification. Engineering Applications of Artificial Intelligence. Volume 151, 110570.
  • Sakal, Colin. , Chen, Tong. , Wu, Wenxin. , Zhang, Wei. , Yang, Yu. & Li, Xinyue. (Apr 2025). Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4–8 hours before sleep onset. Sleep. zsaf113. doi:https://doi.org/10.1093/sleep/zsaf113
  • Wang, Jun. , Wu, Likang. , Liu, Qi. & Yang, Yu. (Mar 2025). An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation. Knowledge-Based Systems. Volume 312, 113133.
  • Zhao, Ziwei. , Yang, Yu. , Yin, Zikai. , Xu, Tong. , Zhu, Xi. , Lin, Fake. , Li, Xueying. & Chen, Enhong. (Dec 2024). Adversarial Attack and Defense on Discrete Time Dynamic Graphs. IEEE Transactions on Knowledge and Data Engineering. Volume 36, Issue 12. 7600 - 7611.
  • Hu, Yang. , Yang, Yu. & Wu, Feng. (Jul 2024). Dynamic cloud manufacturing service composition with re-entrant services: an online policy perspective. International Journal of Production Research. Volume 62, Issue 9. 3263 - 3287. doi:10.1080/00207543.2023.2230317
  • Zhang, Hongbin. , Zhang, Qixin. , Wu, Feng. & Yang, Yu. (Mar 2024). Dynamic Assortment Selection under Inventory and Limited Switches Constraints. IEEE Transactions on Knowledge and Data Engineering. Volume 36, Issue 3. 1056 - 1068. doi:10.1109/TKDE.2023.3301649
  • Zhang, Hongbin. , Yang, Yu. & Wu, Feng. (Jan 2024). Scheduling a set of jobs with convex piecewise linear cost functions on a single-batch-processing machine. OMEGA-International Journal of Management Science. Volume 122, 102958. doi:10.1016/j.omega.2023.102958
  • Wang, Jun. , Yang, Yu. , Liu, Qi. , Fang, Zheng. , Sun, Shujuan. & Xu, Yabo. (Dec 2023). An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat. IEEE Transactions on Computational Social Systems. Volume 10, Issue 6. 3228 - 3240. doi:10.1109/TCSS.2022.3204177
  • Zhang, Hongbin. , Yang, Yu. & Wu, Feng. (Apr 2022). Just-in-time single-batch-processing machine scheduling. Computers & Operations Research. Volume 140. 105675 doi:10.1016/j.cor.2021.105675
  • Hu, Yang. , Wu, Feng. , Yang, Yu. & Wu, Yongkui. (Apr 2022). Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach. Journal of Manufacturing Systems. 63. 593 - 608. doi:https://doi.org/10.1016/j.jmsy.2022.05.008
  • Yang, Yu. & Pei, Jian. (2021). Influence Analysis in Evolving Networks: A Survey. IEEE Transactions on Knowledge and Data Engineering. Volume: 33, Issue: 3. 1045 - 1063. doi:10.1109/TKDE.2019.2934447
  • Yang, Yu. , Mao, Xiangbo. , Pei, Jian. & He, Xiaofei. (May 2020). Continuous Influence Maximization. ACM Transactions on Knowledge Discovery from Data. Volume 14, No. 3, Article 29 (May 2020). 1 - 38. doi:10.1145/3380928
  • Zhu, Xiang. , Wang, Zhefeng. , Yang, Yu. , Bin, Zhou. & Yan, Jia. (2018). Influence efficiency maximization: How can we spread information efficiently?. Journal of Computational Science. 28. 245 - 256. doi:10.1016/j.jocs.2017.11.001
  • Wang, Zhefeng. , Yang, Yu (joint first author). , Chu, Lingyang. , Pei, Jian. & Chen, Enhong. (2017). Activity Maximization by Effective Information Diffusion in Social Networks. IEEE Transactions on Knowledge and Data Engineering. Volume 29, Issue 11. 2374 - 2387. doi:10.1109/TKDE.2017.2740284
  • Liu, Qi. , Xiang, Biao. , Yuan, Nicholas. Jing. , Chen, Enhong. , Xiong, Hui. , Zheng, Yi. & Yang, Yu. (2017). An Influence Propagation View of PageRank. ACM Transactions on Knowledge Discovery from Data. Volume 11, Issue 3. - Article No. 30. doi:10.1145/3046941
  • Yang, Yu. , Pei, Jian. & Al-Barakati, Abdullah. (2017). Measuring In-Network Node Similarity Based on Neighborhoods: A Unified Parametric Approach. Knowledge and Information Systems. Volume 53, Issue 1. 43 - 70. doi:10.1007/s10115-017-1033-5
  • Yang, Yu. , Wang, Zhefeng. , Pei, Jian. & Chen, Enhong. (2017). Tracking Influential Individuals in Dynamic Networks. IEEE Transactions on Knowledge and Data Engineering. Volume 29, Issue 11. 2615 - 2628. doi:10.1109/TKDE.2017.2734667

Conference Paper

  • Tang, Longtao. , Zhou, Ying. & Yang, Yu. (Aug 2025). Lossless Set Compression by Binary Tree Coding. In Proceedings of the 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025). Toronto. Canada: .
  • Yang, Yifan. , Chang, Zhaowei. , Hu, Jia. , Lai, Ming-lai. , Kim, Sung. , Gong, Wei. & Yang, Yu. (Jul 2025). OPSensing: Embracing Real-Time PPG Monitoring with Intraoral Wearables. In Proceedings of the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2025). Copenhagan. Denmark: .
  • Hu, Yang. , Wu, Feng. , Li, Xin. & Yang, Yu. (May 2025). Unanticipated replenishment: online policy for dynamic service composition in manufacturing cloud. In Proceedings of the 2025 SIAM International Conference on Data Mining (SDM 2025). (pp. 288 - 296). Alexandria, Virginia. USA: .
  • Zhang, Qixin. , Wan, Zongqi. , Yang, Yu. , Shen, Li. & Tao, Dacheng. (Apr 2025). Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency. In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025). Singapore: .
  • Liang, Zhicheng. , Yang, Yu. , Ke, Xiangyu. , Xiao, Xiaokui. & Gao, Yunjun. (Aug 2024). A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs. In Proceedings of the 50th International Conference on Very Large Data Bases (VLDB 2024). (pp. 3666 - 3679). Guangzhou. China: .
  • Yang, Yifan. , Feng, Yunyun. , Gong, Wei. & Yang, Yu. (May 2024). Efficient LTE Backscatter with Uncontrolled Ambient Traffic. In Proceedings of 2024 IEEE International Conference on Computer Communications (INFOCOM'2024). (pp. 1781 - 1790). Vancouver. Canada: .
  • Zhang, Qixin. , Deng, Zengde. , Jian, Xiangru. , Chen, Zaiyi. , Hu, Haoyuan. & Yang, Yu. (Oct 2023). Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'2023). (pp. 3330 - 3339). Birmingham. United Kingdom: .
  • Zhang, Qixin. , Ye, Wenbing. , Chen, Zaiyi. , Hu, Haoyuan. , Chen, Enhong. & Yang, Yu. (Jul 2023). Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests. In Proceedings of the 40th International Conference on Machine Learning (ICML'2023). PMLR 202. (pp. 41786 - 41818).
  • Yang, Yifan. , Gong, Wei. & Yang, Yu. (Jun 2023). Ambient Backscatter with a Single Commodity AP. IEEE/ACM International Symposium on Quality of Service 2023 (IWQoS 2023). (pp. 1 - 10). Orlando, FL. USA: doi:10.1109/IWQoS57198.2023.10188740
  • Zhang, Qixin. , Deng, Zengde. , Chen, Zaiyi. , Zhou, Kuangqi. , Hu, Haoyuan. & Yang, Yu. (Apr 2023). Online Learning for Non-monotone DR-Submodular Maximization: From FullInformation to Bandit Feedback. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS'2023). PMLR 206. (pp. 3515 - 3537). València. Spain: .
  • Tang, Longtao. , Zhou, Ying. & Yang, Yu. (Dec 2022). Sequence-to-Set Generative Models. In Proceedings of the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS'2022)).
  • Zhang, Qixin. , Deng, Zengde. , Chen, Zaiyi. , Hu, Haoyuan. & Yang, Yu. (Jul 2022). Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function. In Proceedings of the 39th International Conference on Machine Learning (ICML'22). PMLR 162. (pp. 26116 - 26134). Baltimore MD. USA: .
  • Wu, Tongwen. , Yang, Yu. , Li, Yanzhi. , Mao, Huiqiang. , Li, Liming. , Wang, Xiaoqing. & Deng, Yuming. (Aug 2021). Representation Learning for Predicting Customer Orders. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21). (pp. 3735 - 3744). Singapore. Singapore: .
  • Jin, Tianyuan. , Yang, Yu. , Yang, Renchi. , Shi, Jieming. , Huang, Keke. & Xiao, Xiaokui. (Jun 2021). Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization. In Proceedings of the 47th International Conference on Very Large Data Bases (VLDB'21). (pp. 1756 - 1768). Copenhagen. Denmark: .
  • Cong, Zicun. , Chu, Lingyang. , Yang, Yu. & Pei, Jian. (2021). Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test. In Proceedings of the 47th International Conference on Very Large Data Bases (VLDB'21). (pp. 1583 - 1596). Copenhagen. Denmark: .
  • Chu, Lingyang. , Zhang, Yanyan. , Yang, Yu. , Wang, Lanjun. & Pei, Jian. (2020). Online Density Bursting Subgraph Detection from Temporal Graphs. In Proceedings of the 46th International Conference on Very Large Data Bases (VLDB’20). (pp. 2353 - 2365). Tokyo. Japan: .
  • Chu, Lingyang. , Wang, Zhefeng. , Pei, Jian. , Zhang, Yanyan. , Yang, Yu. & Chen, Enhong. (2019). Finding Theme Communities from Database Networks. In Proceedings of the 45th International Conference on Very Large Data Bases (VLDB’19). (pp. 1071 - 1084). Los Angeles. USA: .
  • Yang, Yu. , Wang, Zhefeng. , Jin, Tianyuan. , Pei, Jian. & Chen, Enhong. (2019). Tracking Top-k Influential Users with Relative Errors. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM'19). (pp. 1783 - 1792). Beijing. China: .
  • Yang, Yu. , Chu, Lingyang. , Zhang, Yanyan. , Wang, Zhefeng. , Pei, Jian. & Chen, Enhong. (2018). Mining Density Contrast Subgraphs. In Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE'18). (pp. 221 - 232). Paris. France: doi:10.1109/ICDE.2018.00029
  • Yang, Yu. , Mao, Xiangbo. , Pei, Jian. & He, Xiaofei. (2016). Continuous Influence Maximization: What Discounts Should We Offer to Social Network Users?. In Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD'16). (pp. 727 - 741). San Francisco. USA: doi:10.1145/2882903.2882961
  • Wang, Zhefeng. , Chen, Enhong. , Liu, Qi. , Yang, Yu. , Ge, Yong. & Chang, Biao. (2015). Maximizing the Coverage of Information Propagation in Social Networks. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15). (pp. 2104 - 2110). Buenos Aires. Argentina: .
  • Xiang, Biao. , Liu, Qi. , Chen, Enhong. , Xiong, Hui. , Zheng, Yi. & Yang, Yu. (2013). PageRank with Priors: An Influence Propagation Perspective. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13). (pp. 2740 - 2746). Beijing. China: .
  • Yang, Yu. , Chen, Enhong. , Liu, Qi. , Xiang, Biao. , Xu, Tong. & Shad, Shafqat. Ali. (2012). On Approximation of Real-World Influence Spread. In Proceedings of the 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'12). (pp. 548 - 564). Bristol. UK: doi:10.1007/978-3-642-33486-3_35


Academic Services

  • PC Member for
    Top Data Science Conferences
    • SIGMOD: International Conference on Management of Data
    • Proceedings of the VLDB Endowment
    • International Conference on Machine Learning
    • Neural Information Processing Systems
    • International Conference on Learning Representations
    Other Well-Known Conferences
    • SIGKDD Conference on Knowledge Discovery and Data Mining
    • The Web Conference
    • IEEE International Conference on Data Engineering
    • ACM SIGIR Conference on Research and Development in Information Retrieval
    • ACM International Conference on Web Search and Data Mining
    • ACM International Conference on Information and Knowledge Management
    • SIAM International Conference on Data Mining
    Editorial Board
    • Associate Editor for ACM Transactions on Knowledge Discovery from Data (TKDD)
    • Associate Editor for Frontiers In Big Data

    Journal Reviewer for
    • IEEE Transactions on Knowledge and Data Engineering (TKDE)
    • ACM Transactions on Knowledge Discovery from Data (TKDD)
    • Data Mining and Knowledge Discovery (DMKD)
    • Knowledge and Information Systems (KAIS)
    • INFORMS Journal on Computing (JOC)


Algorithmic Data Science Group & Openings

  • I am fortunate to work with a group of talented Ph.D. students and Research Assistants to solve challenging and crucial algorithmic problems in data science:
    • Mr. Yang HU (joint Ph.D. student with XJTU, 2021.9-present, BS from NUAA)
    • Mr. Yang LI (joint Ph.D. student with SEU, 2024.9-present, BS from HUST)
    • Miss Jiawei LIU (joint Ph.D. student with USTC, 2024.9-present, BS from USTC)
    • Mr. Yulin LIU (Ph.D. student, 2024.9-present, BS from SJTU)
    • Mr. Kanglong QIAN (Ph.D. student, 2024.9-present, BS from CityU HK, MS from HKU)
    • Mr. Longtao TANG (Ph.D. student, 2020.9-present, BS from USTC)
    • Mr. Yifan YANG (joint Ph.D. student with USTC, 2022.9-present, BS from USTC)
    • Mr. Lyuyi ZHU (Ph.D. student, 2021.9-present, BS from ZJU)
    Alumni
    • Dr. Jun WANG (Ph.D. in Data Science, 2020-2024, first job: PostDoc Fellow at UMN)
    • Dr. Guang YANG (Ph.D. in Data Science, 2022-2024, co-advised with Long FENG, first job: PostDoc Fellow at CUHK)
    • Dr. Hongbin ZHANG (joint Ph.D. with XJTU, 2020-2024, first job: PostDoc Fellow at UBC)
    • Dr. Qixin ZHANG (Ph.D. in Data Science, 2020-2024, first job: PostDoc Fellow at NTU)
    • Mr. Xiangru JIAN (RA, 2020.9-2022.8, MS from CityU HK, next hop: PhD student in CS, University of Waterloo)
    • Mr. Zhicheng LIANG (RA, 2022.8-2023.7, MS from CityU HK, next hop: PhD student in CS, CUHK-Shenzhen)

    Openings
    I am looking for highly motivated PhD students and Postdoc fellows. Please send me your CV and transcripts if you are interested. Due to the high volume of emails, I may not be able to reply to each of them. However, I do read every applicant's email. Please do not be offended if I do not reply.

    I am not interested in applying "fancy" deep nets and tricks in "interesting" applications. Potential research topics for students who want to work with me include, but are not limited to:
    • Discrete choice models
    • Stochastic, online, and combinatorial optimization problems in Operations Management
    • General graph mining and learning
    • AI for Healthcare Management, Smart Medical Wearables
    I expect students to have a strong background in probability & statistics, algorithm design & analysis, optimization and programming.

    Disclaimer: CityU SGS has stringent (though stupid) requirements on the GPA of each applicant's first-degree. The thresholds are as follows: 75 (C9 & QS/THE/ARWU Top 20), 80 (985 & QS/THE/ARWU Top 100), 85 (211 & QS/THE/ARWU Top 200), and 90 (other universities). Note that these are the minimum requirements. To be shortlisted by our school's PhD admission committee, applicants are recommended to have a first-degree GPA of at least 5 points more than the minimum grade.


Last update date : 19 May 2025