KDD 2021 Celebrates Winning Teams of 25th Annual KDD Cup

Across Three Competition Tracks, KDD Cup 2021 Tackled Multi-Datasets in a Time Series, Large-Scale Graph Machine Learning Challenge Solutions, and Intelligent Algorithms to Strategize Vehicle Traffic Demand

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ACM SIGKDD

Oct 19, 2021, 11:30 ET

SAN DIEGO, Oct. 19, 2021 /PRNewswire/ -- The Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) today recognized the winning teams of this year's KDD Cup, the annual competition held at KDD 2021, the premier interdisciplinary conference in data science. KDD Cup 2021, which took place virtually Aug. 14-18, 2021, crowdsourced participants who are helping to solve challenges within the knowledge discovery and data mining industry, providing a platform for aspiring and experienced data scientists alike to build their professional profiles and network with leading professionals in the field during KDD 2021.

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"Every year, the KDD Cup attracts the brightest data science talent across the globe, and competitors in this year's competition did not hold back. All entries demonstrated ingenuity, impact, and impeccable teamwork. I thank all of the teams this year and congratulate those that rose to the top—teams that fundamentally challenged themselves and the way the world thinks when it comes to data science," said Wei Wang, SIGKDD chair and professor in computer science at the University of California, Los Angeles.

This year's competition was supported by several companies and universities who support the data science ecosystem including Facebook AI, TU Dortmund, Intel, Shanghai Jiao Tong University, Yunqi Academy of Engineering, Stanford University, Hexagon-ML, and Tianrang. More than 1,100 teams competed in the City Brain Challenge, 193 teams in the Time Series, and 143 teams in the Open Graph Benchmark (OGB) Large Scale Challenge (LSC), with competition winners selected by an entirely automated process. KDD Cup 2021 winners include:

  • KDD Cup Track 1: Multi-Dataset Time Series Anomaly Detection — Creating New Benchmarks for Time Series Anomaly Detection
    • First place went to DeepBlueAI, which included Zhixing He, Xu Zhang, and Jin Wang.
      • Second place went to Huawei Noah's Ark Lab, which included Marcus Kalander and Sixiao Yang.
      • Third place went to Hitchi America R&D, which included Qiyao Wang, Wei Huang, Ahmed Farahat, and Haiyan Wang.
  • KDD Cup Track 2: Open Graph Benchmark (OGB) Large Scale Challenge (LSC) for Graph Machine Learning — Developing a State-of-the-Art Machine Learning Graph Model for Massive Modern Datasets
    • First place in adopted recent advanced technique for predicting papers' subject areas in a heterogeneous academic graph went to BD-PGL, which included Yunsheng Shi, Zhengjie Huang, Weibin Li, Weiyue Su, and Shikun Feng.
      • Second place went to Academic, which included Petar Velickovic, Peter Battaglia, Jonathan Godwin, Alvaro Sanchez, David Budden, Shantanu Thakoor, Jacklynn Stott, Ravichandra Addanki, Thomas Keck, and Andreea Deac.
      • Third place went to Synerise AI, which included Michal Daniluk, Jacek Dabrowski, Konrad Goluchowski, and Barbara Rychalska.
    • First place for better performance and use for predicting missing facts in a knowledge graph went to BD-PGL, which included Weiyue Su, Shikun Feng, Zeyang Fang, Huijuan Wang, Siming Dai, Hui Zhong, Yunsheng Shi, and Zhengjie Huang.
      • Second place went to OhMyGod, which included Weihua Peng.
      • Third place went to GraphMIRAcles, which included Jianyu Cai, Jiajun Chen, Taoxing Pan, Zhanqiu Zhang, and Jie Wang.
    • First place for predicting a quantum property of molecular graphsdataset and prediction task went to MachineLearning, which included Chengxuan Ying, Mingqi Yang, Shengjie Luo, Tianle Cai, Guolin Ke, Di He, Shuxin Zheng, Chenglin Wu, Yuxin Wang, and Yanming Shen.
      • Second place went to SuperHelix, which included Shanzhuo Zhang, Lihang Liu, Sheng Gao, Donglong He, Weibin Li, Zhengjie Huang, Weiyue Su, and Wenjin Wang.
      • Third place went to Quantum, which included Peter Velickovic, Peter Battaglia, Jonathan Godwin, Alvaro Sanchez, David Budden, Shantanu Thakoor, Jaclynn Stott, Ravichandra Addanki, Sibon Li, and Andreea Deac.
  • KDD Cup Track 3: City Brain Challenge — Providing a City-Scale Road Network and its Traffic Demand
    • First place went to IntelligentLight, which included Gang Song, Zheng Zhang, Chufan Wang, Yucheng Gu, and Zeyu Sun.
      • Second place went to GoodGoodStudy, which included Zhenzhe Ying, Zhuoer Xu, Hui Li, Haotian Wang, and Shiwen Cui.
      • Third place went to 4QC_team, which included Chuan Qin.

The 26th Annual KDD Cup will take place in conjunction with KDD 2022 on Aug. 14-18, 2022 in Washington, D.C. For additional information on this year's cup and winners, please contact  [email protected].

About ACM SIGKDD: 
ACM is the premier global professional organization for researchers and professionals dedicated to the advancement of science and the practice of knowledge discovery and data mining. SIGKDD is ACM's Special Interest Group on Knowledge Discovery and Data Mining. The annual KDD International Conference on Knowledge Discovery and Data Mining is the premier interdisciplinary conference for data mining, data science and analytics.

For more information on KDD, please visit: https://www.kdd.org/.

Follow KDD on:
Facebook—https://www.facebook.com/SIGKDD
Twitter—https://twitter.com/kdd_news
LinkedIn—https://www.linkedin.com/groups/160888/

SOURCE ACM SIGKDD

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