Renjie Wu

Ph.D in CS Ph.D in CS at UCR Ph.D in Computer Science at University of California, Riverside Ph.D in Computer Science
University of California, Riverside

I am looking for full-time job opportunities as Software Engineer. I am proficient in C++, Java and C#. I have experiences in designing user interface with native code, handling network protocols down to data link layer, building full-stack applications into production, etc. My curriculum vitae is available on Linkedin.

About me

I received Ph.D in Computer Science from UC Riverside in 2024. 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 1,232.9 hours just in the air and travelled 744,037 kilometers by plane, that is 18.6x around Earth or 1.94x to the moon!

  • Education
    • University of California, Riverside
      2017 - 2024 · Ph.D 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 2023 · 9th SIGKDD International Workshop on Mining and Learning from Time Series
      • KDD MiLeTS 2022 · 8th 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
  • Selected Honors & Awards
    • Best Paper Runner-Up Award
      Nov. 2023 · 23rd IEEE International Conference on Data Mining (ICDM2023)
    • NSF Student Travel Award
      Oct. 2022 · 22nd IEEE International Conference on Data Mining (ICDM2022)
    • Member of Tau Beta Pi
      Jun. 2021 · California AB Chapter, Tau Beta Pi Association
    • 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

  • 2024
    • Jul. 26, 2024: My Ph.D dissertation “Problems with Problems in Data Ming” is available online on UC eScholarship.
    • May. 22, 2024: I successfully defended my Ph.D dissertation.
    • Mar. 20, 2024: Our paper “C22MP: The Marriage of Catch22 and the Matrix Profile creates a Fast, Efficient and Interpretable Anomaly Detector” is accepted by Springer KAIS.
  • 2023
    • Nov. 14, 2023: Our paper “Matrix Profile XXIX: C22MP: Fusing catch22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector” received best paper runner-up award in IEEE ICDM2023.
    • Sep. 3, 2023: Our paper “Matrix Profile XXIX: C22MP: Fusing catch22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector” is accepted by IEEE ICDM2023.
  • 2022
    • Dec. 15, 2022: Our paper “DAMP: Accurate Time Series Anomaly Detection on Trillions of Datapoints and Ultra-fast Arriving Data Streams” is accepted by Springer DMKD.
    • Sep. 2, 2022: Our paper “Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series” is accepted by IEEE ICKG2022.
    • May. 18, 2022: 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, 2022: 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, 2022: Extended abstract of “When is Early Classification of Time Series Meaningful?” is accepted by IEEE ICDE2022.
  • 2021
    • Sep. 9, 2021: Our paper “Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress” is accepted by IEEE TKDE.
    • Aug. 24, 2021: Our paper “When is Early Classification of Time Series Meaningful?” is accepted by IEEE TKDE.
    • Aug. 14, 2021: 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, 2021: Our time series anomaly detection contest is now live. Go to https://compete.hexagon-ml.com/practice/competition/39/ for details.
    • Jan. 25, 2021: Extended abstract of “FastDTW is approximate and Generally Slower than the Algorithm it Approximates” is accepted by IEEE ICDE2021.
  • 2020
    • Oct. 23, 2020: Our paper “FastDTW is approximate and Generally Slower than the Algorithm it Approximates” is accepted by IEEE TKDE.

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)
    23rd 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
    13th 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
    28th 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
    38th 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
    38th 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
    37th IEEE International Conference on Data Engineering (ICDE2021)
    ↪ [doi] [pdf] [supporting page]