Prof. Reynold Cheng is currently the Division Head and Professor (Computer Science), at the School of Computing and Data Science, in the University of Hong Kong (HKU). He is also a Steering Committee Member of the HKU Musketeers Foundation Institute of Data Science, and the Chairperson of The Transport Technology Committee of the HKU Institute of Transport Studies. He is an academic advisor to the College of Professional and Continuing Education of HKPU. He was an Associate Dean of Engineering in 2022-24. His research interests are in data science, big graph analytics and uncertain data management.

Professor Cheng is named the AI 2000 Most Influential Scholar Honorable Mention in Database in 2023 and 2024. He received the ACM Distinguished Membership Award and the HKU Outstanding Research Student Supervisor Award in 2023. He was listed as the World’s Top 2% Scientists by Stanford University in 2022. He received the SIGMOD Research Highlights Reward 2020, HKICT Awards (2021, 2023), and HKU Knowledge Exchange Award (Engineering) (2024, 2021). He was granted an Outstanding Young Researcher Award 2011-12 by HKU. He received the Universitas 21 Fellowship in 2011, and two Performance Awards from HKPU Computing in 2006 and 2007. He was a PC co-chair of IEEE ICDE 2021. He is on the editorial board of IS, DAPD and DSEJ.

• Chenhao Ma, Reynold Cheng, Laks V. S. Lakshmanan, and Xiaolin Han. “Finding locally densest subgraphs: a convex programming approach.” Proc. VLDB Endow. 15, 11 (July 2022), 2719–2732. https://doi.org/10.14778/3551793.3551826, (2022).
• Xiaolin Han, Reynold Cheng, Chenhao Ma, and Tobias Grubenmann. “DeepTEA: effective and efficient online time-dependent trajectory outlier detection.” Proc. VLDB Endow. 15, 7 (March 2022), 1493–1505. https://doi.org/10.14778/3523210.3523225, (2022).
• Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V. S. Lakshmanan, and Xiaolin Han. “A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery.” In Proceedings of the 2022 International Conference on Management of Data (SIGMOD ’22). Association for Computing Machinery, New York, NY, USA, 845–859. https://doi.org/10.1145/3514221.3517837, (2022).
• Xiaolin Han, Reynold Cheng, Tobias Grubenmann, Silviu Maniu, Chenhao Ma, and Xiaodong Li. “Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks.” SIAM International Conference on Data Mining (SDM 2022), April 2022. https://epubs.siam.org/doi/pdf/10.1137/1.9781611977172.8, (2022).
• Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V. S. Lakshmanan, Wenjie Zhang, and Xuemin Lin. “On Directed Densest Subgraph Discovery.” ACM Trans. Database Syst. 46, 4, Article 13 (December 2021), 45 pages. https://doi.org/10.1145/3483940, (2021).
• Xiaodong Li, Reynold Cheng, Kevin Chen-Chuan Chang, Caihua Shan, Chenhao Ma, and Hongtai Cao. “On analyzing graphs with motif-paths.” Proc. VLDB Endow. 14, 6 (February 2021), 1111–1123. https://doi.org/10.14778/3447689.3447714, (2021).
• Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, and Reynold Cheng. “Fast augmentation algorithms for network kernel density visualization.” Proc. VLDB Endow. 14, 9 (May 2021), 1503–1516. https://doi.org/10.14778/3461535.3461540, (2021).
• Dan He, Sibo Wang, Xiaofang Zhou, and Reynold Cheng. “GLAD: A Grid and Labeling Framework with Scheduling for Conflict-Aware kkNN Queries.” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1554-1566, 1 April 2021, DOI: 10.1109/TKDE.2019.2942585, (2021).
• Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V. S. Lakshmanan, Wenjie Zhang, and Xuemin Lin. “On Directed Densest Subgraph Discovery.” ACM Trans. Database Syst. 46, 4, Article 13 (December 2021), 45 pages. https://doi.org/10.1145/3483940, (2021).
• Zichen Zhu, Tsz Nam Chan, Reynold Cheng, Loc Do, Zhipeng Huang, and Haoci Zhang. “Effective and Efficient Discovery of Top-k Meta Paths in Heterogeneous Information Networks.” In the Transactions on Knowledge and Data Engineering (IEEE TKDE) (Early access), Nov 2020, DOI: 10.1109/TKDE.2020.3037218, (2021).

Databases science, graph databases, uncertain data management