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Weiren Yu

Brief Biography

Weiren Yu is an Assistant Professor in the Department of Computer Science at the University of Warwick, and an Honorary Visiting Fellow in the Department of Computing at Imperial College. Prior to that, he was a Lecturer of Computer Science in the School of Engineering and Applied Science at Aston University.

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”.

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).

Research Interests

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.

Selected Publications [Full Publications in DBLP]

  • W. Yu, X. Lin, W. Zhang, J. Pei, J. McCann. SimRank*: Effective and scalable pairwise similarity search based on graph topology. The VLDB Journal, 28(3), pp. 401-426, 2019
  • W. Yu, X. Lin, W. Zhang, and J. McCann. Dynamical SimRank Assessment on Time-Varying Networks. The VLDB Journal. 79-104. 2018. [PDF]
  • W. Yu, and F. Wang. Fast Exact CoSimRank Search on Evolving and Static Graphs. The 27th International World Wide Web Conference (WWW '18). Lyon, France, pp. 599-608, 2018.
  • X. Ren, C. Yu, W. Yu, S. Yang, X. Yang, J. McCann, and P. S. Yu. LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy. IEEE Transactions on Information Forensics & Security (IEEE TIFS), pp. 2151-2166, 2018. [PDF]
  • W. Yu, and J. McCann. Random Walk with Restart over Dynamic Graphs. The 15th IEEE International Conference on Data Mining (IEEE ICDM '16). Barcelona, Spain, pp.589-598, 2016. [PDF]
  • W. Yu, and J. McCann. Efficient Partial-Pairs SimRank Search on Large Graphs. The 41st International Conference on Very Large Data Base (VLDB '15). Hawaii, USA, pp. 569-580, 2015. [PDF]
  • W. Yu, and J. McCann. High Quality Graph-Based Similarity Search. The 38th ACM SIGIR International Conference (ACM SIGIR '15). Santiago, Chile, pp. 83-93, 2015. [PDF]
  • W. Yu, and J. McCann. Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Scores. The 53rd Annual Meeting of the Association for Computational Linguistics. (ACL '15). Beijing, China, pp. 327-334, 2015.
  • W. Yu, X. Lin, and W. Zhang. Fast Incremental SimRank on Link-Evolving Graphs. The 30th IEEE International Conference on Data Engineering (IEEE ICDE '14), Chicago, USA, pp. 304-315, 2014. [PDF]
  • W. Yu, and J. McCann. Sig-SR: SimRank Search over Singular Graphs.The 37th ACM SIGIR International Conference (ACM SIGIR '14), Brisbane, Australia, 2014 [PDF]
  • W. Yu, X. Lin, W. Zhang, and J. McCann. Fast All-Pairs SimRank Assessment on Large Graphs and Bipartite Domains. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 27(7): 1810-1823, 2014. [PDF]
  • W. Yu, X. Lin, and W. Zhang. Towards Efficient SimRank Computation on Large Networks. The 29th IEEE International Conference on Data Engineering (IEEE ICDE '13), Brisbane, Australia, pp. 601-612, 2013. [PDF]
  • W. Yu, X. Lin, W. Zhang, L. Chang, and J Pei. More is Simpler: Effectively and Efficiently Assessing Node-Pair Similarities Based on Hyperlinks. The 40th International Conference on Very Large Data Base (VLDB '13), Hangzhou, China, pp. 13-24, 2013. [PDF]
  • W. Yu, and X. Lin. IRWR: Incremental Random Walk with Restart. The 36th ACM SIGIR International Conference (ACM SIGIR '13), Dublin, Ireland, 2013 [PDF]
  • W. Yu, X. Lin, W. Zhang, Y. Zhang, and J. Le. SimFusion+: Extending SimFusion Towards Efficient Estimation on Large and Dynamic Networks. The 35th ACM SIGIR International Conference (ACM SIGIR '12), Portland, USA, pp. 365-374, 2012. [PDF]