Welcome

Curious about how to write my Chinese name? It’s simply 吴仁杰.

About me

I’m currently a 5th year Ph.D student with the Department of Computer Science and Engineering at University of California, Riverside. My advisor is brilliant Dr. Eamonn Keogh. My research interest includes time series analysis, data mining, and machine learning.

  • Education
    • University of California, Riverside
      2017 - Present · Ph.D Student in Computer Science
      2016 - 2017 · Visiting Student in Computer Science
    • Harbin Institute of Technology at Weihai
      2013 - 2017 · B.Sc. in Computer Science and Technology
    • National Taiwan University
      2015 - 2016 · Visiting Student in Computer Science & Information Engineering
  • Honors & Awards
    • Dean’s Distinguished Fellowship
      Sep. 2017 · University of California, Riverside
    • Outstanding Graduate (山东省优秀毕业生)
      May. 2017 · Dept. Human Resources & Social Security of Shandong, China
    • Outstanding Model of Student Leaders (哈工大优秀学生干部标兵)
      Dec. 2014 · Harbin Institute of Technology
    • National Scholarship (国家奖学金)
      Nov. 2014 · Ministry of Education, China
  • Certifications
    • Microsoft Certified System Administrator
      Aug. 2009 · Microsoft
    • Microsoft Certified IT Professional
      Feb. 2009 · Microsoft
    • Microsoft Certified Technology Specialist
      Dec. 2008 · Microsoft

News

2022

  • May. 18: Our paper “Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams” is accepted by ACM SIGKDD2022.
  • Jan. 25: Extended abstract of “Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress” is accepted by IEEE ICDE2022.
  • Jan. 25: Extended abstract of “When is Early Classification of Time Series Meaningful?” is accepted by IEEE ICDE2022.

2021

2020

  • Oct. 23: Our paper “FastDTW is approximate and Generally Slower than the Algorithm it Approximates” is accepted by IEEE TKDE.

Publications

Conference

  • Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams
    Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn Keogh
    28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2022)
    ↪ [supporting page]

  • Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract)
    Renjie Wu, Eamonn J. Keogh
    38th IEEE International Conference on Data Engineering (ICDE2022), pp. 1479-1480
    ↪ [pdf] [supporting page]

  • When is Early Classification of Time Series Meaningful? (Extended Abstract)
    Renjie Wu, Audrey Der, Eamonn J. Keogh
    38th IEEE International Conference on Data Engineering (ICDE2022), pp. 1477-1478
    ↪ [pdf] [supporting page]

  • FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract)
    Renjie Wu, Eamonn J. Keogh
    37th IEEE International Conference on Data Engineering (ICDE2021), pp. 2327-2328
    ↪ [pdf] [doi] [supporting page]

Journal

  • When is Early Classification of Time Series Meaningful?
    Renjie Wu, Audrey Der, Eamonn J. Keogh
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
    ↪ [pdf] [doi] [supporting page]

  • Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress
    Renjie Wu, Eamonn J. Keogh
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
    ↪ [pdf] [doi] [supporting page]

  • FastDTW is approximate and Generally Slower than the Algorithm it Approximates
    Renjie Wu, Eamonn J. Keogh
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020
    ↪ [pdf] [doi] [supporting page]