1. PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning
    Jie Feng, Can Rong, Funing Sun, Diansheng Guo, Yong Li
    IMWUT/UbiComp 2020 (Full paper) PDF
  2. DeepMM: Deep Learning Based Map Matching with Data Augmentation
    Kai Zhao, Jie Feng, Zhao Xu, Tong Xia, Lin Chen, Funing Sun, Diansheng Guo, Depeng Jin, Yong Li
    SIGSPATIAL 2019 (Poster paper) PDF Link
  3. DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data
    Jie Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, Depeng Jin
    WWW 2019 (Full paper, Accept rate: 18%) Codes PDF Link
  4. DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
    Ziqian Lin*, Jie Feng*, Ziyang Lu, Yong Li, Depeng Jin
    AAAI 2019 (Full paper, Accept rate: 16.2%) Codes PDF Link * Equal contribution
  5. DeepDPM: Dynamic Population Mapping via Deep Neural Network
    Zefang Zong*, Jie Feng*, Kechun Liu, Hongzhi Shi, Yong Li
    AAAI 2019 (Full paper, Accept rate: 16.2%) PDF Link * Equal contribution
  6. DeepMove: Predicting Human Mobility with Atentional Recurrent Networks
    Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin.
    WWW 2018 (Full paper, Accept rate: 14.8%) Codes PDF Link
  7. DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction
    Jie Feng, Xinlei Chen, Rundong Gao, Ming Zeng, Yong Li
    IEEE Network, 2018, 32 (6), 108-115 PDF Link
  8. A Bimodal Model to Estimate Dynamic Metropolitan Population by Mobile Phone Data
    Jie Feng, Yong Li, Fengli Xu, Depeng Jin
    Sensors, 2018, 18 (10), 3431 PDF Link
  9. Uniqueness in the City: Urban Morphology and Location Privacy
    Hancheng Cao, Jie Feng, Yong Li, Vassilis Kostakos
    Ubicomp 2018 PDF Link
  10. Context-aware Real-time Population Estimation for Metropolis
    Fengli Xu, Jie Feng, Pengyu Zhang, Yong Li
    Ubicomp 2016 (Honorable mention award) PDF Link Project