Welcome

About me

I received Ph.D in Computer Science from UC Riverside. My advisor is brilliant Dr. Eamonn Keogh. My research interest includes time series analysis, data mining, and machine learning. My Ph.D dissertation “Problems with Problems in Data Mining” is available on UC eScholarship.

I like travel, especially by rail and air. I’ve visited all 50 states (plus Washington D.C.) in the U.S., all 4 U.S. unincorporated organized territories (Puerto Rico, U.S. Virgin Islands, Northern Mariana Islands, and Guam), and 11 countries around the world. I’ve spent 1,290.4 hours just in the air and traveled 770,781 kilometers by plane, that is 19.2x around Earth or 1.0x round trip to the moon!

• Education

  • University of California, Riverside
    Ph.D in Computer Science
  • University of California, Riverside
    M.S. in Computer Science
  • Harbin Institute of Technology at Weihai
    B.Sc. in Computer Science and Technology
• Professional Services

  • Program Committee (PC) Member
    • KDD MiLeTS 2025 · 11^th SIGKDD International Workshop on Mining and Learning from Time Series
    • KDD MiLeTS 2023 · 9^th SIGKDD International Workshop on Mining and Learning from Time Series
    • KDD MiLeTS 2022 · 8^th SIGKDD International Workshop on Mining and Learning from Time Series
  • Journal Reviewer
    • TPAMI · IEEE Transactions on Pattern Analysis and Machine Intelligence
    • TKDE · IEEE Transactions on Knowledge and Data Engineering
    • TNNLS · IEEE Transactions on Neural Networks and Learning Systems
    • TII · IEEE Transactions on Industrial Informatics
    • DMKD · Springer Journal of Data Mining and Knowledge Discovery
    • KAIS · Springer Journal of Knowledge and Information Systems
    • TIST · ACM Transactions on Intelligent Systems and Technology
    • BDR · Elsevier Journal of Big Data Research
• Certifications

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

Publications

Dissertation

• Problems with Problems in Data Mining
  Renjie Wu (Advisor: Eamonn Keogh)
  Ph.D dissertation, University of California Riverside, eScholarship, 2024
  ↪ [permalink] [pdf]

Journal

• C²²MP: The Marriage of Catch22 and the Matrix Profile creates a Fast, Efficient and Interpretable Anomaly Detector
  Sadaf Tafazoli, Yue Lu, Renjie Wu, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn Keogh
  Springer Journal of Knowledge and Information Systems (KAIS), 2024
  ↪ [doi] [supporting page]

• DAMP: Accurate Time Series Anomaly Detection on Trillions of Datapoints and Ultra-fast Arriving Data Streams
  Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn Keogh
  Springer Journal of Data Mining and Knowledge Discovery (DMKD), 2022
  ↪ [doi] [pdf] [supporting page]

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

Conference

• Matrix Profile XXIX: C²²MP: Fusing catch22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector
  Sadaf Tafazoli, Yue Lu, Renjie Wu, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn Keogh (Best Paper Runner-Up)
  23^rd IEEE International Conference on Data Mining (ICDM2023)
  ↪ [doi] [pdf] [supporting page]

• Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
  Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn Keogh
  13^th IEEE International Conference on Knowledge Graph (ICKG2022)
  ↪ [doi] [pdf] [supporting page]

• 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)
  ↪ [doi] [pdf] [poster] [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)
  ↪ [doi] [pdf] [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)
  ↪ [doi] [pdf] [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)
  ↪ [doi] [pdf] [supporting page]