Biography

Cheng LONG is an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore. He received his PhD from the Hong Kong University of Science and Technology (HKUST) in 2015.

His research spans databases and machine learning, with a current focus on vector databases (for supporting AI applications such as RAG, LLM inference, and agent memory) and machine learning and data management/mining on spatial data, time series, and graphs. He leads the VectorDB @NTU group. See Publications for more details of his research.

Research Areas

Vector Databases
Vector quantization, ANN index, and retrieval for high-dimensional vectors powering modern AI applications including RAG.
Machine Learning
Deep learning for spatial data, time series forecasting, and graph representation learning.
Data Management & Mining
RL-based indexing, dense subgraph mining, trajectory data management, and spatial query processing.

Recent News

  • Feb 2026 Paper IVF-RaBitQ (GPU) — a GPU-native vector search method that combines IVF and RaBitQ — posted at arXiv.
  • Jan 2026 Award Received the CCDS Research Award (Young Faculty), given by the College of Computing and Data Science (CCDS), NTU.
  • Jun 2025 Release Launched RaBitQ Library — an open-source library for advanced quantization (RaBitQ) enabling high-accuracy, low-memory vector search.
  • Sep 2024 Paper Multi-bit RaBitQ — quantizing high-dimensional vectors with multi bits per dimension — accepted at SIGMOD 2025.
  • May 2024 Paper 1-bit RaBitQ — quantizing high-dimensional vectors with 1 bit per dimension — accepted at SIGMOD 2024.

Selected Awards

  • 2026 CCDS Research Award (Young Faculty), College of Computing and Data Science, NTU
  • 2024 Best Demo Runner Up Award, ICDE 2024
  • 2014 Fulbright-RGC Hong Kong Research Scholar Award, Research Grants Council (RGC), Hong Kong
  • 2013 Prof. Francis Chin Research Award (Best Research Award), ACM Hong Kong
  • 2013 Overseas Research Award, HKUST
  • 2012 Professor Samuel Chanson Teaching Assistant Award, HKUST
  • 2012 Champion, 6th Postgraduate Paper Contest, IEEE Hong Kong Computational Intelligence Chapter

Selected Talks

  • High-Dimensional Vector Quantization: General Framework, Recent Advances, and Future Directions
    CCDS Research Fest (2026) · Alibaba Cloud (2025) · International Workshop on Frontiers of Data Engineering and AI (2025)
  • 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)
  • Dense Subgraph Mining: Applications, Problems, and Algorithms
    KDD.SG (2023) · WeBank (2022)