学术报告：Approximate Nearest Neighbor Search in High-Dimensional Vector Databases

报告题目：Approximate Nearest Neighbor Search in High-Dimensional Vector Databases

报告时间：2023年9月19日下午3:00

报告地点：计算机学院B404会议室

报告人：周晓方Xiaofang Zhou

报告人国籍：澳大利亚

报告人单位：香港科技大学

报告人简介：Professor Xiaofang Zhou is Otto Poon Professor of Engineering and Chair Professor of Computer Science and Engineering at The Hong Kong University of Science and Technology. Currently, he is Head of Department of Computer Science and Engineering and Co-Director of Big Data Institute. He is the founding director of HKUST-HKPC Joint Lab on Industrial AI and Robotics Research, HKUST-China Unicom Joint Lab on Smart Society, and JC STEM Lab on Data Science Foundations. He has been working in data science, spatiotemporal databases, data mining, data quality management, high-performance query processing, big data analytics, and machine learning, co-authored over 500 research papers. He received Best Paper Awards from WISE 2012&2013, ICDE 2015&2019, DASFAA 2016 and ADC 2019. He was Program Committee Chair of IEEE International Conference on Data Engineering (ICDE 2013), ACM International Conference on Information and Knowledge Management (CIKM 2016), and International Conference on Very Large Databases (PVLDB 2020). Professor Zhou is a Global STEM Scholar of Hong Kong and an IEEE Fellow.

报告摘要：Approximate nearest neighbor search is an important research topic with a wide range of applications. In this talk, we first introduce this problem and review the major research results in the past. We discuss the current work in the database research community, categorizing the work by their key underlying methodologies such as locality-sensitive hashing and approximate nearest neighbor graphs. Finally, we examine several new directions, with a focus on vector databases to support large language models.

邀请人：彭智勇、王黎维