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.

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

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.

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.

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

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)
↪ [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] [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] [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]

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]