Welcome

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

About me

I’m currently a 5^th 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.

I like travel, especially by rail and air. I’ve visited all 50 states in the U.S. and 11 countries in the world. I’ve spent 851.5 hours just in the air and travelled 545,672 kilometers by plane, that is 13.6x around Earth or 1.42x to the moon!

• 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
• Professional Services

  • Program Committee (PC) Member
    • KDD MiLeTS 2022 · 8^th SIGKDD International Workshop on Mining and Learning from Time Series
  • Journal Reviewer
    • TKDE · IEEE Transactions on Knowledge and Data Engineering
    • DMKD · Springer Journal of Data Mining and Knowledge Discovery
    • TIST · ACM Transactions on Intelligent Systems and Technology
• Selected 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 “Matrix Profile XXIV: 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

• Sep. 9: Our paper “Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress” is accepted by IEEE TKDE.
• Aug. 24: Our paper “When is Early Classification of Time Series Meaningful?” is accepted by IEEE TKDE.
• Aug. 14: The Hexagon ML/UCR Time Series Anomaly Archive is now available: https://www.cs.ucr.edu/~eamonn/time_series_data_2018/UCR_TimeSeriesAnomalyDatasets2021.zip.
• Mar. 15: Our time series anomaly detection contest is now live. Go to https://compete.hexagon-ml.com/practice/competition/39/ for details.
• Jan. 25: Extended abstract of “FastDTW is approximate and Generally Slower than the Algorithm it Approximates” is accepted by IEEE ICDE2021.

2020

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

Publications

Conference

• Matrix Profile XXIV: 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
  28^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2022)
  ↪ [pdf] [supporting page]

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

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

• FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract)
  Renjie Wu, Eamonn J. Keogh
  37^th 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]