Dr. Yu YANG

I am an Assistant Professor with the School of Data Science at City University of Hong Kong. I obtained my Ph.D. in Computing Science from Simon Fraser University in Feb 2019. Before that, I obtained my M.E. from University of Science and Technology of China in 2013, and my B.E. from Hefei University of Technology in 2010, both in Computer Science.

My 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. I also have strong interests in machine learning theory, especially in applying learning theory to accelerate data processing. Openings in my group.

Selected Publications

[Google Scholar]

[ _ indicates my advisees, † indicates student collaborators in other groups, * indicates equal contribution]

Papers in Refereed Journals

  1. Ziwei Zhao†, Yu Yang, Zikai Yin†, Tong Xu, Xi Zhu†, Fake Lin†, Xueying Li and Enhong Chen. "Adversarial Attack and Defense on Discrete Time Dynamic Graphs". To appear in IEEE Transactions on Knowledge and Data Engineering.
  2. Hongbin Zhang, Yu Yang and Feng Wu. "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, January 2024, 102958.
  3. Hongbin Zhang, Qixin Zhang, Feng Wu and Yu Yang. "Dynamic Assortment Selection under Inventory and Limited Switches Constraints". IEEE Transactions on Knowledge and Data Engineering, Volume 36, Issue 3, pages 1056-1068, March 2024, IEEE Computer Society.
  4. Yang Hu, Yu Yang and Feng Wu. "Dynamic Cloud Manufacturing Service Composition with Re-entrant Services: An Online Policy Perspective". International Journal of Production Research, Volume 62, 2024 - Issue 9.
  5. Jun Wang, Yu Yang, Qi Liu, Zheng Fang, Shujuan Sun and Yabo Xu. "An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat". IEEE Transactions on Computational Social Systems, Volume 10, Issue 6, pages 3228-3240, December 2023.
  6. Yang Hu, Feng Wu, Yu Yang and Yongkui Liu. "Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach". Journal of Manufacturing Systems, Volume 63, April 2022, Pages 593-608.
  7. Hongbin Zhang, Yu Yang and Feng Wu. "Just-in-time single-batch-processing machine scheduling". Computers & Operations Research, Volume 140, April 2022, 105675.
  8. Yu Yang and Jian Pei. "Influence Analysis in Evolving Networks: A Survey". IEEE Transactions on Knowledge and Data Engineering, Volume 33, Issue 3, pages 1045-1063, March 2021, IEEE Computer Society.
  9. Yu Yang, Xiangbo Mao, Jian Pei and Xiaofei He. "Continuous Influence Maximization". ACM Transactions on Knowledge Discovery from Data, Volume 14, No. 3, Article 29 (May 2020). 1 - 38.
  10. Xiang Zhu, Zhefeng Wang, Yu Yang, Bin Zhou and Yan Jia. "Influence efficiency maximization: How can we spread information efficiently?". Journal of Computational Science, 28 (2018): 245-256.
  11. Zhefeng Wang*, Yu Yang*, Jian Pei, Lingyang Chu and Enhong Chen. "Activity Maximization by Effective Information Diffusion in Social Networks". IEEE Transactions on Knowledge and Data Engineering, Volume 29, Issue 11, pages 2374-2387, November 2017, IEEE Computer Society.
  12. Yu Yang, Zhefeng Wang, Jian Pei and Enhong Chen. "Tracking Influential Individuals in Dynamic Networks" [full version]. IEEE Transactions on Knowledge and Data Engineering, Volume 29, Issue 11, pages 2615-2628, November 2017, IEEE Computer Society.
  13. Qi Liu, Biao Xiang, Nicholas Jing Yuan, Enhong Chen, Hui Xiong, Yi Zheng, and Yu Yang, "An Influence Propagation View of PageRank". ACM Transactions on Knowledge Discovery from Data, Volume 11, Issue 3, Article No. 30, April 2017, ACM Press.
  14. Yu Yang, Jian Pei and Abdullah Al-Barakati. "Measuring In-Network Node Similarity Based on Neighborhoods: A Unified Parametric Approach". Knowledge and Information Systems, Volume 53, Issue 1, pages 43–70, October 2017, Springer-Verlag.

Papers in Refereed Conferences

  1. Zhicheng Liang*, Yu Yang*, Xiangyu Ke, Xiaokui Xiao and Yunjun Gao. "A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs". Accepted for the 50th International Conference on Very Large Data Bases (VLDB 2024).
  2. Yifan Yang, Yunyun Feng, Wei Gong and Yu Yang. "Efficient LTE Backscatter with Uncontrolled Ambient Traffic". In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM 2024).
  3. Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen, Haoyuan Hu and Yu Yang. "Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization". In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023): 3330–3339, Birmingham, United Kingdom, October 21-25, 2023.
  4. Qixin Zhang*, Wenbing Ye*, Zaiyi Chen*, Haoyuan Hu, Enhong Chen and Yu Yang. "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 202:41786-41818.
  5. Yifan Yang, Wei Gong and Yu Yang. "Ambient Backscatter with a Single Commodity AP". In Proceedings of the IEEE/ACM International Symposium on Quality of Service 2023 (IWQoS 2023): 1-10, Orlando, FL, USA, June 19-21, 2023.
  6. Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu and Yu Yang. "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).
  7. Longtao Tang, Ying Zhou and Yu Yang. "Sequence-to-Set Generative Models". In Proceedings of the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022).
  8. Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu and Yu Yang. "Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function". In Proceedings of the 39th International Conference on Machine Learning (ICML 2022), PMLR 162:26116-26134.
  9. Tongwen Wu†, Yu Yang, Yanzhi Li, Huiqiang Mao, Liming Li, Xiaoqing Wang, and Yuming Deng. "Representation Learning for Predicting Customer Orders". In Proceedings of the 2021 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21): 3735–3744, Singapore, August 14-18, 2021.
  10. Tianyuan Jin†, Yu Yang, Renchi Yang, Jieming Shi, Keke Huang, and Xiaokui Xiao. "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): 1756-1768, Copenhagen, Denmark, August 16-20, 2021.
  11. Zicun Cong†, Lingyang Chu, Yu Yang, and Jian Pei. "Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test". In Proceedings of the 47th International Conference on Very Large Data Bases (VLDB’21): 1583-1596, Copenhagen, Denmark, August 16-20, 2021.
  12. Lingyang Chu, Yanyan Zhang, Yu Yang, Lanjun Wang and Jian Pei. "Online Density Bursting Subgraph Detection from Temporal Graphs". In Proceedings of the 46th International Conference on Very Large Data Bases (VLDB’20): 2353-2365, Tokyo, Japan, Aug. 31-Sept. 4, 2020.
  13. Yu Yang, Zhefeng Wang, Tianyuan Jin, Jian Pei and Enhong Chen. "Tracking Top-k Influential Users with Relative Errors". In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM'19): 1783-1792, Beijing, China, Nov. 3-7, 2019.
  14. Lingyang Chu, Zhefeng Wang, Jian Pei, Yanyan Zhang, Yu Yang and Enhong Chen. "Finding Theme Communities from Database Networks". In Proceedings of the 45th International Conference on Very Large Data Bases (VLDB’19): 1071-1084, Los Angeles, USA, Aug. 26-30, 2019.
  15. Yu Yang, Lingyang Chu, Yanyan Zhang, Zhefeng Wang, Jian Pei and Enhong Chen. "Mining Density Contrast Subgraphs". In Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE'18): 221-232, Paris, France, April 16-19, 2018.
  16. Yu Yang, Xiangbo Mao, Jian Pei and Xiaofei He. "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): 727-741, San Francisco, USA, June 26-July 1, 2016. [Data][Code].
  17. Zhefeng Wang, Enhong Chen, Qi Liu, Yu Yang, Yong Ge and Biao Chang. "Maximizing the Coverage of Information Propagation in Social Networks". In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15): 2104-2110, Buenos Aires, Argentina, July 25-31, 2015.
  18. Biao Xiang, Qi Liu, Enhong Chen, Hui Xiong, Yi Zheng and Yu Yang. "PageRank with Priors: An Influence Propagation Perspective". In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13): 2740-2746, Beijing, China, August 3-9, 2013.
  19. Yu Yang, Enhong Chen, Qi Liu, Biao Xiang, Tong Xu and Shafqat Ali Shad. "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 (ECMLPKDD'12): 548-564, Bristol, UK, September 24-28, 2012.

Academic Services

  • Editorial Board

    • Associate Editor for ACM Transactions on Knowledge Discovery from Data (TKDD)
    • Associate Editor for Frontiers In Big Data

  • PC Member

      Top Data Science Conferences
    • SIGMOD: International Conference on Management of Data
    • Proceedings of the VLDB Endowment
    • Neural Information Processing Systems
    • International Conference on Learning Representations
    • SIGKDD Conference on Knowledge Discovery and Data Mining
      Other Well-Known Conferences
    • 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

  • Journal Reviewer

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

Teaching

  • SDSC5003 - Storing and Retrieving Data (CityU HK), Instructor
  • SDSC3001 - Big Data: The Arts and Science of Scaling (CityU HK), Instructor
  • SDSC3002 - Data Mining (CityU HK), Instructor
  • SDSC2004/GE2343 - Data Visualization (CityU HK), Instructor

Algorithmic Data Science Group

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)

    • Acknowledgement

    Our research is generously supported by City University of Hong Kong, Hong Kong RGC, Hong Kong Institute for Data Science, Alibaba Group, DataStory, etc.

    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.