Bio

Ph.D. Arizona State University, 2019M.Sc. University of Alberta, 2014B.Eng. Zhejiang University, 2012

Jundong Li is an Assistant Professor in the Department of Electrical and Computer Engineering, with a joint appointment in the Department of Computer Science, and School of Data Science. He received his Ph.D. degree in Computer Science at Arizona State University in 2019, M.Sc. degree in Computer Science at University of Alberta in 2014, and B.Eng. degree in Software Engineering at Zhejiang University in 2012. His research interests are generally in data mining and machine learning, with a particular focus on graph mining, causal inference, and algorithmic fairness. As a result of his research work, he has published over 100 papers in high-impact venues (including KDD, WWW, IJCAI, AAAI, WSDM, EMNLP, CIKM, ICDM, SDM, ECML-PKDD, CSUR, TPAMI, TKDE, TKDD, TIST, etc), with over 5,500 citation count. He has won several prestigious awards, including NSF CAREER Award, JP Morgan Chase Faculty Research Award, Cisco Faculty Research Award, and being selected for the AAAI New Faculty Highlights roster. His group's research is generously supported by NSF (CAREER, III, SaTC, SAI), JLab, JP Morgan, and Cisco.

Awards

  • NSF CAREER Award 2022
  • UVA ECE Department Faculty Research Award 2022
  • J.P. Morgan AI Faculty Research Award 2022
  • J.P. Morgan AI Faculty Research Award 2021
  • Cisco Faculty Research Award 2021
  • AAAI New Faculty Highlights 2021
  • INFORMS QSR Best Refereed Paper Finalist 2019
  • INFORMS QSR Best Student Paper Finalist 2019

Research Interests

  • Data Mining
  • Machine Learning
  • Artificial Intelligence

Selected Publications

  • "Task-Adaptive Few-shot Node Classification", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li
  • "Learning Causal Effects on Hypergraphs", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, Jaime Teevan
  • "On Structural Explanation of Bias in Graph Neural Networks", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li
  • "FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs", International Joint Conference on Artificial Intelligence (IJCAI), 2022 Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li
  • "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks", The Web Conference (formerly WWW), 2022 Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li
  • "Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US", The Web Conference (formerly WWW), 2022 Jing Ma, Yushun Dong, Zheng Huang, Daniel Mietchen, Jundong Li
  • "Learning Fair Node Representations with Graph Counterfactual Fairness", ACM International Conference on Web Search and Data Mining (WSDM), 2022 Jing Ma, Ruocheng Guo, Mengting Wan, Longqi Yang, Aidong Zhang, Jundong Li
  • "AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter", ACM International Conference on Information and Knowledge Management (CIKM), 2021 Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li
  • "Individual Fairness for Graph Neural Networks: A Ranking based Approach", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021 Yushun Dong, Jian Kang, Hanghang Tong, Jundong Li
  • "Unsupervised Graph Alignment with Wasserstein Distance Discriminator", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021 Ji Gao, Xiao Huang, Jundong Li
  • "Multi-Cause Effect Estimation with Disentangled Confounder Representation", International Joint Conference on Artificial Intelligence (IJCAI), 2021 Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li
  • "Deconfounding with Networked Observational Data in a Dynamic Environment", ACM International Conference on Web Search and Data Mining (WSDM), 2021 Jing Ma, Ruocheng Guo, Chen Chen, Aidong Zhang, Jundong Li
  • "Learning Individual Causal Effects from Networked Observational Data", ACM International Conference on Web Search and Data Mining (WSDM), 2020 Ruocheng Guo, Jundong Li, Huan Liu
  • "A Survey of Learning Causality with Data: Problems and Methods", ACM Computing Surveys (CSUR), 2020 Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu
  • "Adaptive Unsupervised Feature Selection on Attributed Networks", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019 Jundong Li, Ruocheng Guo, Chenghao Liu, Huan Liu
  • "Unsupervised Personalized Feature Selection", AAAI Conference on Artificial Intelligence (AAAI), 2018 Jundong Li, Liang Wu, Harsh Dani, Huan Liu
  • "Attributed Network Embedding for Learning in a Dynamic Environment", ACM International Conference on Information and Knowledge Management (CIKM), 2017 Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu
  • "Label Informed Attributed Network Embedding", ACM International Conference on Web Search and Data Mining (WSDM), 2017 Xiao Huang, Jundong Li, Xia Hu
  • "Feature Selection: A Data Perspective", ACM Computing Surveys (CSUR), 2017 Feature Selection: A Data Perspective

Featured Grants & Projects

  • NSF CAREER Program

    CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making


    This project develops a suite of novel causal inference models and algorithms to analyze observational data by harnessing the power of human knowledge and gaining deeper insights to advance personalized decision making.

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  • NSF III Core Program

    III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications


    The primary goal of this project is to develop novel operations to improve the essential building blocks of deep learning algorithms for graphs, propelling the state-of-the-art graph mining and deep learning research to a new frontier and advancing graph-related applications from different disciplines.

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  • NSF SaTC Core Program

    SaTC: CORE: Small: Empowering Network Attack Detection with Complex Graph Modeling


    This project develops new attack detection approaches for large networks using complex graph modeling.

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