Summary
Dr Weiren Yu is an Honorary Visiting Fellow in the Department of Computing at Imperial College. He is also an Assistant Professor in the Department of Computer Science at the University of Warwick.
Weiren received the Ph.D. degree from the School of Computer Science and Engineering at the University of New South Wales (UNSW, Sydney). During his years at UNSW, he was also a Research Assistant at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and National ICT Australia (NICTA). After that, he spent two years as a Postdoctoral Researcher at the Adaptive Embedded Systems Engineering (AESE) Laboratory in the Department of Computing, Imperial College. He collaborated with NEC Europe Ltd and the Department of Civil and Environmental Engineering at Imperial, working on an IoT project “Big Data Technologies for Smart Water Systems”.
Weiren’s research interests span the area of large-scale data mining, web information retrieval, graph data management, stream databases, and spatial/temporal databases. He is interested in developing effective and efficient data analysis techniques for novel data intensive applications.
He is a recipient of seven Best Paper Awards, including one Best Research Paper Award for ECSA 2016, two CiSRA (Canon Information Systems Research Australia) Best Research Paper Awards for ICDE 2014 and VLDB 2013 respectively, one One of the Best Papers of ICDE in 2013, and three Best (Student) Paper Awards for APWEB 2010, WAIM 2010 and WAIM 2011, respectively. He is a member of the IEEE and ACM.
He has served on various editorial boards, and as PC (e.g., PVLDB 2021 PC) and an active reviewer of international journals (e.g., The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Information Forensics and Security, ACM Transactions on Knowledge Discovery from Data, World Wide Web Journal, Sensors) and top conferences (e.g., SIGIR, SIGMOD, VLDB, ICDE, EDBT, CIKM).
Publications
Journals
Yu W, Lin X, Zhang W, et al., 2015, Fast All-Pairs SimRank Assessment on Large Graphs and Bipartite Domains, Ieee Transactions on Knowledge and Data Engineering, Vol:27, ISSN:1041-4347, Pages:1810-1823
Conference
Yu W, Mccann, 2015, Effectively Positioning Water Loss Event in Smart Water Networks, 2nd International Electronic Conference on Sensors and Applications, MDPI, ISSN:1424-8220
Yu W, McCann J, 2015, Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Relevance, The 53rd Annual Meeting of the Association for Computational Linguistics
Yu W, McCann J, 2015, High Quality Graph-Based Similarity Retrieval on Large Graphs, The 38th ACM SIGIR International Conference, ACM