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 [   ] 

Dr. YANG Yu (楊禹博士)

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

Assistant Professor

Contact Information

Office:  LAU-16-222
Phone: (+852) 3442-4035
Email: yuyang@cityu.edu.hk
Web: Personal Homepage

Research Interests

  • Algorithmic Data Science
  • Large Graphs/Networks
  • 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 mining and data science, with an emphasis on devising effective and efficient algorithmic tools for large-scale graphs. 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.


Publications Show All Publications Show Prominent Publications


Journal

  • 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
  • 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

  • Wu, Tongwen. , Yang, Yu. , Li, Yanzhi. , Mao, Huiqiang. , Li, Liming. , Wang, Xiaoqing. & Deng, Yuming. (in press). 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. (in press). 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



Openings

  • I am looking for highly motivated PhD students. 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:
    1. Submodular optimization and applications
    2. Choice models for subset selection
    3. Representation learning and generative models for graphs
    4. Approximate nearest neighbor search in high-dimensional spaces
    5. Combinatorial optimization problems in Operations Management
    6. General graph mining and learning
    I expect students to have a strong background in probability & statistics, algorithm design & analysis, optimization and programming.


Last update date : 05 Jan 2022