报告题目：High-Dimensional Vector Quantization:General Framework, Recent Advances, and Future Directions

报告时间：2025年12月17日上午10:30

报告地点：Avidol B404会议室

报告人：Cheng LONG

报告人单位：新加坡南洋理工大学

报告人简介：Cheng LONG is an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU). He earned his Ph.D. degree from Hong Kong University of Science and Technology (HKUST) in 2015 and his B.Eng. degree from South China University of Technology in 2010. He has research interests broadly in data management and data mining. More recently, he has been working in high-dimensional vector data management (and its applications in large models such as retrieval-augmented generative AI). His work has garnered recognition and accolades, including the prestigious "Best Research Award" from ACM-Hong Kong, the "Fulbright-RGC Research Award" granted by the Research Grant Council (Hong Kong), the "PG Paper Contest Award" bestowed by IEEE-HK, and the "Overseas Research Award" received from HKUST.

报告摘要：High-dimensional vector data lies at the core of numerous modern applications,from recommendation systems to large-scale retrieval and retrieval-augmented generation. Effectively managing and processing such data presents both significant opportunities and challenges. A key enabler in this context is vector quantization, which compresses high-dimensional vectors while preserving essential similarities. In this talk, I will begin by exploring why vector quantization is crucial for scalable and efficient high-dimensional vector management. I will then present the general framework of vector quantization and discuss existing popular schemes. Building on this foundation, I will introduce RaBitQ, a recent advance that provides optimized approaches for binary and scalar quantization and achieves asymptotic optimality. RaBitQ has been incorporated into multiple major production-level vector databases and search engines of Meta, ByteDance, Elastic, Apple, Alibaba, Milvus, OceanBase, etc. The talk will conclude with a discussion on future research directions of vector quantization.

邀请人：晏潇、江佳伟