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Fangyuan ZHANG is a Ph.D. Candidate at Database Group of the Chinese University of Hong Kong advised by Professor Sibo WANG. Previously, he obtained his Bachelor's degree from ACM Class at Shanghai Jiao Tong University in 2021.

His research interests include algorithms for big data analytics and processing.

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• Research Intern, Microsoft Research Asia
  Beijing, China, Jul 2020 - Jan 2021
  Advised by researcher Bin Zhu.
• Security Research Intern, Tencent
  Shanghai, China, Jul 2019 - Sep 2019
  Advised by researcher Tianyi Xie.

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1. Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite Networks
   Fangyuan Zhang, Dechuang Chen, Sibo Wang, Yin Yang, Junhao Gan
   Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), to appear, 2024.
2. Efficient Approximation Framework for Attribute Recommendation
   Xingguang Chen, Fangyuan Zhang, Jinchao Huang, Sibo Wang
   Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), to appear, 2024.
3. Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach.
   Qintian Guo, Chen Feng, Fangyuan Zhang, Sibo Wang
   Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), to appear, 2024.
4. Efficient Dynamic Weighted Set Sampling and Its Extension
   Fangyuan Zhang, Mengxu Jiang, Sibo Wang
   Proceedings of the VLDB Endowment (PVLDB), to appear, 2023.
5. Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme
   Guanhao Hou, Qintian Guo, Fangyuan Zhang, Sibo Wang, Zhewei Wei
   Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 1(1): 25:1-25:26, 2023.
6. Effective Indexing for Dynamic Structural Graph Clustering
   Fangyuan Zhang, Sibo Wang
   Proceedings of the VLDB Endowment (PVLDB), 15(11): 2908-2920, 2022
7. Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling
   Xingguang Chen, Fangyuan Zhang, Sibo Wang
   Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pages 157-167, 2022.