Cheng Long

LONG Cheng is currently an Assistant Professor at the School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU). From 2016 to 2018, he worked as a lecturer (Asst Professor) at Queen's University Belfast, UK. He received his PhD degree from the Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2015, and his BEng degree from South China University of Technology (SCUT), China, in 2010.

LONG Cheng has research interests broadly in data management, data mining and machine learning. In particular, he has passion in designing (1) algorithms for querying and mining geo-spatial data, spatio-temporal data and graph data and (2) models for learning from these data types for various applications such as smart cities, logistics, social media analytics, etc.

His research has been recognized with the "Fulbright-RGC Research Award" provided by Research Grant Council of Hong Kong (2014), the "Overseas Research Award" provided by HKUST (2014), the "Best Research Award" provided by ACM-Hong Kong (2013), and the "Paper Contest Award" (Champion) provided by IEEE-HK (2012).

Research Interests

• Spatio-temporal data management, mining and learning
• Graph data mining
• Big data analytics
• Urban informatics and computing

Current Grants

• Collaborative AI “Human-AI Collaboration, Knowledge Representation, Transfer Learning”
• Google PhD Fellowship
• Inferring Tourism Statistics from Publically Available Users' Geo-Social Footprint Data
• Learn to Augment and Represent Spatial Urban Data
• Leveraging Machine Learning for Bipartite Matching
• Pre-Processing and Querying Big Trajectory Data with Reinforcement Learning

Articles (Journal)

Wang, Zheng, Cheng Long, Gao Cong, and Yiding Liu. "Efficient and effective similar subtrajectory search with deep reinforcement learning." Proceedings of the VLDB Endowment 13, no. 12 (2020): 2312-2325.

Wei, Victor Junqiu, Raymond Chi-Wing Wong, and Cheng Long. "Architecture-Intact Oracle for Fastest Path and Time Queries on Dynamic Spatial Networks." In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 1841-1856. 2020.

Ruan, Sijie, Zi Xiong, Cheng Long, Yiheng Chen, Jie Bao, Tianfu He, Ruiyuan Li, Shengnan Wu, Zhongyuan Jiang, and Yu Zheng. "Doing in One Go: Delivery Time Inference Based on Couriers' Trajectories." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2813-2821. 2020.

Ruan, Sijie, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, and Yu Zheng. "Learning to generate maps from trajectories." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 890-897. 2020.

Chen, Yile, Cheng Long, Gao Cong, and Chenliang Li. "Context-aware deep model for joint mobility and time prediction." In Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 106-114. 2020.

Wang, Zheng, Cheng Long, Gao Cong, and Ce Ju. "Effective and efficient sports play retrieval with deep representation learning." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 499-509. 2019.

Chan, Harry Kai-Ho, Cheng Long, Da Yan, and Raymond Chi-Wing Wong. "Fraction-score: a new support measure for co-location pattern mining." In 2019 IEEE 35th international conference on data engineering (ICDE), pp. 1514-1525. IEEE, 2019.

Wang, Yansheng, Yongxin Tong, Cheng Long, Pan Xu, Ke Xu, and Weifeng Lv. "Adaptive dynamic bipartite graph matching: A reinforcement learning approach." In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1478-1489. IEEE, 2019.