Cui, Bin


Cui, Bin


Research Interests: Database, data mining

Office Phone: 86-10-6276 5825


Cui, Bin is a professor in the Department of Computer Science and technology, School of EECS, and has served as the Director of Institute of Network Computing and Information Systems since 2016. He obtained his B.Sc. from Xi'an Jiaotong University in 1996, and Ph.D. from National University of Singapore in 2004 respectively. His research interests include database system architectures, query and index techniques, big data management and mining.

Dr. Cui has published more than 100 research papers, and most of them are published in top-tier conferences and journals, such as SIGMOD, VLDB, TKDE, and TOIS. He has served in the Technical Program Committee of various international conferences including SIGMOD, VLDB, ICDE and KDD, and as Vice PC Chair of ICDE 2011&2018, Demo Co-Chair of ICDE 2014, Area Chair of VLDB 2014, PC Co-Chair of APWeb 2015 and WAIM 2016. He is serving as a Trustee Board Member of VLDB Endowment, is also in the Editorial Board of VLDB Journal, Distributed and Parallel Databases, and Information Systems, and was an associate editor of TKDE (2009-2013). He was awarded Microsoft Young Professorship award (2008), CCF Young Scientist award (2009), and appointed as Cheung Kong Professor in 2016.

Dr. Cui has more than ten research projects including NSFC, 973 programs, 863 project, etc. His research achievements are summarized as follows:

1) Data organization and indexing techniques: One major research topic in data management field is to make the data management system support more complex data types, such as spatial objects, images and bio-information. He proposed some new data representation methods and indexing structures to facilitate the organization of different types of complex data, designed multi-feature extraction, feature fusing, and dimension transformation techniques to alleviate the dimensionality curse problem. The indexes proposed can efficiently support the query processing and data mining applications.

2) Query processing and optimization: This is the key factor to improve the performance of database systems. Relational database systems can support simple query tasks; however, many complex query tasks are not well supports due to the constraints of relational model and SQL. He focused on the query processing techniques for different query types and system environments, and proposed new solutions for query reformulation, query expression, P2P query processing, and buffer management to improve the performance of database systems.

Data mining techniques on Complex data: The complex data, such as social media data, have some characteristics, including large volume, heterogeneity, rich structure and correlation, which bring great challenge to researchers in data management and mining fields. He proposed new data mining techniques, which integrate both the content and structural features to mine the embedded knowledge of big data. These methods can improve the Internet applications and user experiences, such as search engine, multimedia search and resource recommendation, etc.