Welcome

Welcome to my personal website! Wondering how to write my Chinese name? It’s simply 吴仁杰.

About me

I’m currently a 4th year Ph.D student in 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
  • 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
  • National Scholarship of China
    Nov. 2014 · Ministry of Education of China
• Certifications

  • Microsoft Certified System Administrator
    Aug. 2009 · Microsoft
  • Microsoft Certified IT Professional
    Feb. 2009 · Microsoft
  • Microsoft Certified Technology Specialist
    Dec. 2008 · Microsoft

Publications

• Renjie Wu, Audrey Der, and Eamonn J. Keogh, “When is Early Classification of Time Series Meaningful?,” arXiv, preprint, 2021.
  [Paper (arXiv)] [Supporting page]

• Renjie Wu and Eamonn J. Keogh, “FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract),” 37th IEEE International Conference on Data Engineering (ICDE2021), in press, 2021.
  [Paper (arXiv)] [Paper (doi)] [Supporting page]

• Renjie Wu and Eamonn J. Keogh, “Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress,” arXiv, preprint, 2020.
  [Paper (arXiv)] [Supporting page]

• Renjie Wu and Eamonn J. Keogh, “FastDTW is approximate and Generally Slower than the Algorithm it Approximates,” IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2020.
  [Paper (arXiv)] [Paper (doi)] [Supporting page]