Chenhao Ma

Chenhao Ma 

School of Data Science
The Chinese University of Hong Kong, Shenzhen
Longgang, Shenzhen, Guangdong, China
machenhao (at) cuhk.edu.cn

Bio

Dr. Chenhao Ma is an Assistant Professor at the Chinese University of Hong Kong, Shenzhen. Prior to that, he was a Postdoctoral Fellow at the University of Hong Kong, working with Prof. Reynold Cheng. He received his PhD degree from Department of Computer Science in the University of Hong Kong (HKU) in 2021. He once was a visiting student in the University of New South Wales (UNSW) in 2019. Till now, he has published more than 10 papers in the areas of database and data mining, including one of four Best of SIGMOD2020 (a world flagship conference in database areas, 4/458), and most of them were published in top-tier conferences and journals (e.g., SIGMOD, PVLDB, and TODS). He was awarded the ACM SIGMOD Research Highlight Award 2021. He has served as invited reviewers for several top journals (e.g., TKDE and VLDBJ).

News I am looking for highly self-motivated PhD students, PostDocs, and research assistants. If you are interested in working with me, please feel free to drop me an email. Please check the position info.

Interests

His research interests mainly focus on large-scale data management and data mining, particularly graph data management and traffic data mining.

Working and Education Experience

Publications (* indicates corresponding authors)

In the Year of 2022

  • Densest Subgraph Discovery on Large Graphs: Applications, Challenges, and Techniques
    Yixiang Fang, Wensheng Luo, Chenhao Ma.
    In Proceedings of the VLDB Endowment (PVLDB), 15, 2022.

  • Finding Locally Densest Subgraphs: A Convex Programming Approach
    Chenhao Ma, Reynold Cheng, Laks V.S. Lakshmanan, Xiaolin Han
    In Proceedings of the VLDB Endowment (PVLDB), 15, 2022.

  • Effective Community Search over Large Star-Schema Heterogeneous Information Networks
    Yangqin Jiang, Yixiang Fang, Chenhao Ma, Xin Cao, Chunshan Li
    In Proceedings of the VLDB Endowment (PVLDB), 15, 2022.

  • DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection
    Xiaolin Han, Reynold Cheng, Chenhao Ma* and Tobias Grubenmann
    In Proceedings of the VLDB Endowment (PVLDB), 15(7): 1493-1505, 2022.

  • A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery
    Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V.S. Lakshmanan, Xiaolin Han
    In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 845-859, 2022 .

  • Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks
    Xiaolin Han, Reynold Cheng, Tobias Grubenmann, Silviu Maniu, Chenhao Ma*, Xiaodong Li
    In SIAM International Conference on Data Mining (SDM), pages 64-72, 2022.

  • The Social Technology and Research (STAR) Lab in the University of Hong Kong
    Reynold Cheng, Chenhao Ma, Xiaodong Li, Yixiang Fang, Ye Liu, Victor Y.L. Wong, Esther Lee, Tai Hing Lam, Sai Yin Ho, Man Ping Wang, Weijie Gong, Wentao Ning, Ben Kao
    In ACM SIGMOD Record, 51, 2022.

In the Year of 2021

  • On Directed Densest Subgraph Discovery
    Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V.S. Lakshmanan, Wenjie Zhang, Xuemin Lin
    In ACM Transactions on Database Systems (TODS), 46(4):1-45, 2021.
    Invited as one of four Best of SIGMOD 2020.

  • Efficient Directed Densest Subgraph Discovery
    Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V.S. Lakshmanan, Wenjie Zhang, Xuemin Lin
    In SIGMOD Record, 50(1):33-40, 2021, Special Issue on the 2021 ACM SIGMOD Research Highlight Award.

  • On Analyzing Graphs with Motif-Paths
    Xiaodong Li, Reynold Cheng, Kevin Chen Chuan Chang, Caihua Shan, Chenhao Ma, Hongtai Cao
    In Proceedings of the VLDB Endowment (PVLDB), 14(6): 1111-1123, 2021.

In the Year of 2020

  • Efficient Algorithms for Densest Subgraph Discovery on Large Directed Graphs
    Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V.S. Lakshmanan, Wenjie Zhang, Xuemin Lin
    In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 1051-1066, 2020.
    One of four Best of SIGMOD 2020, rate: 4/458.

In the Year of 2019

  • Motif Paths: A New Approach for Analysing Higher-order Semantics between Graph Nodes
    Xiaodong Li, Tse Nam Chan, Reynold Cheng, Caihua Shan, Chenhao Ma, Kevin Chang
    In HKU Technical Reports, 2019.

  • LINC: A Motif Counting Algorithm for Uncertain Graphs
    Chenhao Ma, Reynold Cheng, Laks V.S. Lakshmanan, Tobias Grubenmann, Yixiang Fang, Xiaodong Li
    In Proceedings of the VLDB Endowment (PVLDB), 13(2): 155-168, 2019.

Professional Services

Selected Honors and Awards

Teaching Experience