(cr. my high school friend Chi Zhang)
Jian Kang
Email: jiank2 [at] illinois [dot] edu
I am a 4th-year PhD student in the Department of Computer Science at University of Illinois at Urbana-Champaign (UIUC) and a member of iDEA Lab led by Dr. Hanghang Tong. My research interests mainly lie in large-scale data mining and machine learning on graphs.
Before joining UIUC, I obtained my BEng degree in Communications Engineering at Beijing University of Posts and Telecommunications and MCS degree in Computer Science at University of Virginia. Then I spent two years at Arizona State University as a PhD student in School of Computing, Informatics, and Decision Systems Engineering.
Publications
Preprint:
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Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong. MultiFair: Multi-Group Fairness in Machine Learning. In Arxiv. [Paper]
Journal Paper:
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Meijia Wang, Jian Kang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong. Graph Ranking Auditing: Problem Definitions and Fast Solutions. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. [Paper]
Conference Papers:
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Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li. Individual Fairness for Graph Neural Networks: A Ranking based Approach. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2021. [Paper]
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Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong. InFoRM: Individual Fairness on Graph Mining. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2020. [Paper] [Slides] [Code]
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Jian Kang, Hanghang Tong. N2N: Network Derivative Mining. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019. [Paper] [Slides] [Code]
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Jian Kang*, Scott Freitas*, Haichao Yu, Yinglong Xia, Nan Cao, Hanghang Tong. X-Rank: Explainable Ranking in Complex Multi-layered Networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), 2018. [Paper]
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Jian Kang, Meijia Wang, Nan Cao, Yinglong Xia, Wei Fan, Hanghang Tong. AURORA: Auditing PageRank on Large Graphs. In Proceedings of 2018 IEEE International Conference on Big Data (Big Data), 2018. [Paper] [Slides] [Code]
(check out the updated results in our TKDE paper) -
Haichao Yu, Jian Kang, Yonglong Xia, Nan Cao, Hanghang Tong. Visual Mining of Multi-sourced Networks. In Proceedings of the 5th China Visualization and Visual Analytics Conference (ChinaVis), 2018.
Education
- PhD (Computer Science), University of Illinois at Urbana-Champaign. 2019.8 - present
- PhD (Computer Science), Arizona State University. 2017.8 - 2019.5
- MCS (Computer Science), University of Virginia. 2015.8 - 2016.12
- BEng (Communications Engineering), Beijing University of Posts and Telecommunications. 2010.9 - 2014.6
Experience
- Research Intern, Facebook AI. May. 2021 - Aug. 2021.
Talks
- Guest lecture on algorithmic fairness at Penn State (DS 402, instructor: Dr. Fenglong Ma).
Services
- Program Committee: AAAI (2021), CIKM (2021), DLG-AAAI (2021), ICLR(2021 -- 2022), ICML (2020 -- 2021), IJCAI (2021 -- 2024), NeurIPS (2021), WSDM (2022)
- External Reviewer: AAAI (2019), ACMMM (2018), CIKM (2018), DASFAA (2018 -- 2019), DSAA (2018), ICML (2019), IJCAI (2019), KDD (2018 -- 2019), NeurIPS (2020), PAKDD (2019), SIGIR (2021), TheWebConf (formerly WWW, 2018, 2020 -- 2021), UAI (2021), WSDM (2019 -- 2020)
- Journal Reviewer: CACM, COMPJ, JMLR, TBD, TKDD, TKDE
Awards
- ICML 2020 Top Reviewer, 2020 [Certificate]
- KDD 2020 Student Travel Award, 2020
- CIKM 2019 Student Travel Award, 2019
- Big Data 2018 Student Travel Award, 2018