Welcome
Curious about how to write my Chinese name? It’s simply 吴仁杰.
About me
I’m currently a final 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 995.8 hours just in the air and travelled 615,507 kilometers by plane, that is 15.4x around Earth or 1.6x 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
- University of California, Riverside
Professional Services
- Program Committee (PC) Member
- KDD MiLeTS 2022 · 8th SIGKDD International Workshop on Mining and Learning from Time Series
- Journal Reviewer
- TNNLS · IEEE Transactions on Neural Networks and Learning Systems
- TPAMI · IEEE Transactions on Pattern Analysis and Machine Intelligence
- 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
- Program Committee (PC) Member
Selected Honors & Awards
- 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
- NSF Student Travel Award
Certifications
- Microsoft Certified System Administrator
Aug. 2009 · Microsoft - Microsoft Certified IT Professional
Feb. 2009 · Microsoft - Microsoft Certified Technology Specialist
Dec. 2008 · Microsoft
- Microsoft Certified System Administrator
News
2022
- Dec. 15: 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: Our paper “Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series” is accepted by IEEE ICKG2022.
- 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 _Time Series Anomaly Datasets2021.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
Journal
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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 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 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, and 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]