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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
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.


Publications Show All Publications Show Prominent Publications


Journal

  • Zhang, Hongbin. , Zhang, Qixin. , Wu, Feng. & Yang, Yu. (in press). Dynamic Assortment Selection under Inventory and Limited Switches Constraints. IEEE Transactions on Knowledge and Data Engineering. doi:10.1109/TKDE.2023.3301649
  • Hu, Yang. , Yang, Yu. & Wu, Feng. (in press). Dynamic cloud manufacturing service composition with re-entrant services: an online policy perspective. International Journal of Production Research. (25 pages) doi:10.1080/00207543.2023.2230317
  • 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. 122. 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
  • Yang, Yu. , Wang, Zhefeng (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

  • Yang, Yifan. , Feng, Yunyun. , Gong, Wei. & Yang, Yu. (in press). Efficient LTE Backscatter with Uncontrolled Ambient Traffic. 2024 IEEE International Conference on Computer Communications (INFOCOM'2024).
  • 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
    • 2018: KDD
    • 2019: KDD
    • 2020: SIGIR, KDD
    • 2021: VLDB, KDD
    • 2022: SIGMOD, KDD, NeurIPS
    • 2023: VLDB, KDD, NeurIPS
    2024: VLDB, ICLR, KDD


    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 Nanjing University of Aeronautics and Astronautics)
    • Mr. Longtao TANG (Ph.D. student, 2020.9-present, BS from University of Science and Technology of China)
    • Mr. Jun WANG (Ph.D. student, 2020.9-present, MS and BS from University of Science and Technology of China)
    • Mr. Yifan YANG (joint Ph.D. student with USTC, 2022.9-present, BS from University of Science and Technology of China)
    • Mr. Hongbin ZHANG (joint Ph.D. student with XJTU, 2020.9-present, BS from China University of Mining and Technology)
    • Mr. Qixin ZHANG (Ph.D. student, 2020.9-present, BS from University of Science and Technology of China)
    • Mr. Lyuyi ZHU (Ph.D. student, 2021.9-present, BS from Zhejiang University)
    Alumni
    • Mr. Xiangru JIAN (RA, 2020.9-2022.8, MS from CityU HK, BS from Tongji University, next hop: PhD student in CS, University of Waterloo)
    • Mr. Zhicheng LIANG (RA, 2022.8-2023.7, MS from CityU HK, BS from Jinan University, 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:
    • Submodular optimization and applications
    • Discrete choice models
    • Stochastic, online, and combinatorial optimization problems in Operations Management
    • Representation learning and generative models for graphs
    • Approximate nearest neighbor search in high-dimensional spaces
    • General graph mining and learning
    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 : 17 Jan 2024