Dongjie Wang

Assistant Professor, University of Kansas.

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Dongjie Wang is an assistant professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. His research interests lie in data-centric AI, causal graph learning, spatial-temporal data mining, user profiling, and graph mining.

He has published over 70 papers in top-tier conferences and journals, including NeurIPS, KDD, AAAI, WWW, TKDE, and KAIS. His work has received multiple recognitions, including three Best Paper Runner-up Awards at SIGSPATIAL and ICDM, and a Spotlight Paper at NeurIPS 2023.

His recent research on automated urban planning has been featured by Synced AI and UCF Today, highlighting the broader societal impact of his work.

He actively serves the research community as a program committee member or reviewer for NeurIPS, ICML, KDD, AAAI, TPAMI, TKDD, and Nature Cities.


For Perspective Students

[TA/RA positions available] We are seeking highly motivated Ph.D. students to join our team. If you’re interested in data mining and its applications, please contact me at wangdongjie100@gmail.com. Include your CV, transcripts, TOEFL & GRE scores, and other relevant materials. Use the subject format “Prospective_Student_PhD_Your Name_…”.

The University of Kansas (KU), the state’s flagship university, is a member of the Association of American Universities (AAU) and recognized as a Research I (R1) institution by the Carnegie Classification. For more details on the Computer Science Ph.D. program application process, visit the website.


Announcement

We will host the third AI for Data Editing at KDD 2025 📊. If you are interested in this topic, we welcome you to submit your paper 📝. For more details, please visit this link.


News

2025/08/05 Congratulations! Two papers have been accepted by CIKM 2025. :fireworks:
2025/05/18 Congratulations! Two papers have been accepted by KDD 2025. :fireworks:
2025/04/30 Congratulations! One paper have been accepted by IJCAI 2025. :fireworks:
2024/12/20 Congratulations! One paper have been accepted by SDM 2025. :fireworks:
2024/12/09 Congratulations! Three papers have been accepted by AAAI 2025. :fireworks:

Selected Publications [See More]

2025

  1. KDD’25
    Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
    Rui Liu, Rui Xie, Zijun Yao, Yanjie Fu, and Dongjie Wang
    In Proceedings of the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

2023

  1. NeurIPS’23
    Reinforcement-enhanced autoregressive feature transformation: Gradient-steered search in continuous space for postfix expressions
    Dongjie Wang, Meng Xiao, Min Wu, Yuanchun Zhou, and Yanjie Fu
    Advances in Neural Information Processing Systems, 2023
  2. KDD’23
    Interdependent Causal Networks for Root Cause Localization
    Dongjie Wang, Zhengzhang Chen, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, and Haifeng Chen
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
  3. KDD’23
    Incremental Causal Graph Learning for Online Root Cause Analysis
    Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, and Haifeng Chen
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
  4. AAAI’23
    Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
    Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, and Yanjie Fu
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2023
  5. Reinforced Imitative Graph Learning for Mobile User Profiling
    Dongjie Wang, Pengyang Wang, Yanjie Fu, Kunpeng Liu, Hui Xiong, and Charles E. Hughes
    IEEE Transactions on Knowledge and Data Engineering, 2023

2022

  1. KDD’22
    Group-wise reinforcement feature generation for optimal and explainable representation space reconstruction
    Dongjie Wang, Yanjie Fu, Kunpeng Liu, Xiaolin Li, and Yan Solihin
    In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022

2021

  1. AAAI’21
    Reinforced imitative graph representation learning for mobile user profiling: An adversarial training perspective
    Dongjie Wang, Pengyang Wang, Kunpeng Liu, Yuanchun Zhou, Charles E Hughes, and Yanjie Fu
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2021