Welcome

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

About me

I’m currently a 4^th 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
- 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

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 ICDE2021.
Oct. 23, 2020: Our paper FastDTW is approximate and Generally Slower than the Algorithm it Approximates is accepted by IEEE TKDE.

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), TKDE Posters Track, 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]