Dr. Cheng LONG

Cheng LONG

Associate Professor
Data Management @Nanyang Tech
College of Computing and Data Science
Nanyang Technological University

Email: c.long@ntu.edu.sg
Office: N4-2c-94
Address: 50 Nanyang Avenue, Singapore, 639798
Telephone: +65 6790 6822

DBLP profile
Google Scholar profile

Openings: We have openings of Post-Doc, PhD students, visiting scholars/students from time to time. Please contact me via email if you have interest.

Biography: Cheng LONG is currently an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore. From 2016 to 2018, he was a lecturer at Queen's University Belfast, UK. He received his PhD degree from the Hong Kong University of Science and Technology, Hong Kong, in 2015, and his BEng degree from South China University of Technology, China, in 2010.

Research Interests: He has research interests broadly in data management and data mining. Specifically, he works in high-dimensional vector data management (and its applications in large models such as retrieval-augmented generative AI), spatial data management with machine learning-based techniques, spatial data mining in the urban domain (e.g., traffic and mobility analysis), and graph data mining (including dense subgraph mining and graphlet mining). For details of his research, please refer to his research summary and publications.

Selected Awards:

  • Best Paper Award of SIGMOD'20 (Nominee), 2020
  • Fulbright-RGC Hong Kong Research Scholar Award, provided by Research Grants Council (RGC) of Hong Kong, 2014
  • Prof. Francis Chin Research Award (Best Research Award), provided by ACM (HK), 2013
  • Overseas Research Award, provided by HKUST, 2013
  • Professor Samuel Chanson Teaching Assistant Award (Best Teaching Assistant Award), provided by HKUST, 2012
  • Champion in 6th Postgraduate Paper Contest, provided by IEEE (HK) Computational Intelligence Chapter, 2012

Selected Talks:

  • RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search, Huawei Singapore Harmony Software Technology Workshop (2024), Huawei Global Software Technology Summit & Sentosa Summit (2024)
  • Nearest Neighbor Search on High-Dimensional Vector Data, Huawei Sentosa Software Technology Summit (2023), TheWebConf’24 Workshop (keynote)
  • RL4SpatialDB: On Leveraging Reinforcement Learning for Spatial Data Management, MCSCT 2023 (Macau), Microsoft Research Lab - Redmond (2023); MUST 2023 (keynote), Southeast U (2023)
  • Dense Subgraph Mining: Applications, Problems, and Algorithms, KDD.SG (2023); Nanjing UST (2023); WeBank (2022)