Lijie Fan

  • Ph.D. student in Computer Science
  • Massachusetts Institute of Technology
  • Email: lijiefan[at]


    I am a final-year PhD student at MIT CSAIL. I'm very fortunate to be advised by Prof. Dina Katabi. I also work closely with Prof. Phillip Isola. Previously I obtained my bachelor’s degree in Computer Science from Tsinghua University.
    I do research in machine learning and its applications, especially:
    • Large scale training of Vision-Language Models
    • Learning from Synthetic data from LLMs and Diffusion Models
    • Multimodal self-supervised learning and alignment
    • Video generation and understanding


    *: equal contribution
    Learning Vision from Models Rivals Learning Vision from Data
    Yonglong Tian*, Lijie Fan*, Kaifeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola
    CVPR 2024  PDF / arXiv / code
    Scaling Laws of Synthetic Images for Model Training ... for Now
    Lijie Fan*, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian*
    CVPR 2024  PDF / arXiv / code
    Improving CLIP Training with Language Rewrites
    Lijie Fan*, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian*
    NeurIPS 2023  PDF / arXiv / code
    StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
    Yonglong Tian*, Lijie Fan*, Phillip Isola, Huiwen Chang, Dilip Krishnan
    NeurIPS 2023  PDF / arXiv / code / MIT News
    Reparo: Loss-Resilient Generative Codec for Video Conferencing
    Tianhong Li, Vibhaalakshmi Sivaraman, Lijie Fan, Mohammad Alizadeh, Dina Katabi
    Pre-print  PDF / arXiv
    Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
    Mingyu Ding, Yikang Shen, Lijie Fan, Zhenfang Chen, Zitian Chen, Ping Luo, Joshua B Tenenbaum, Chuang Gan
    CVPR 2023  PDF / arXiv / code
    Making Contrastive Learning Robust to Shortcuts
    Tianhong Li*, Lijie Fan*, Yuan Yuan, Hao He, Yonglong Tian, Rogerio Feris, Piotr Indyk, Dina Katabi
    WACV 2023  PDF / arXiv / Talk (by Dina)
    Targeted supervised contrastive learning for long-tailed recognition
    Tianhong Li*, Peng Cao*, Yuan Yuan, Lijie Fan, Yuzhe Yang, Rogerio Feris, Piotr Indyk, Dina Katabi
    CVPR 2022  PDF / arXiv / code
    Unsupervised Learning for Human Sensing Using Radio Signals
    Tianhong Li*, Lijie Fan*, Yuan Yuan*, Dina Katabi
    WACV 2022  PDF / arXiv
    When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
    Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Chuang Gan
    NeurIPS 2021   Project Page / PDF / arXiv / Code / TechTalks
    Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
    Lijie Fan, Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong
    AAAI 2019  Project Page/ PDF / arXiv
    Oral Presentation
    End-to-End Learning of Motion Representation for Video Understanding
    Lijie Fan*, Wenbing Huang*, Chuang Gan, Stefano Ermon, Boqing Gong, Junzhou Huang
    CVPR 2018  Project Page/ PDF / arXiv / Code / Talk
    Spotlight Presentation
    Towards Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks
    Wenbing Huang*, Lijie Fan* ,Mehrtash Harandi, Lin Ma, Huaping Liu, Wei Liu, Chuang Gan
    IEEE Transactions on Image Processing (T-IP) 2019   PDF

    Adversarial Localization Network
    Lijie Fan, Shengjia Zhao, Stefano Ermon
    NIPS 2017 Workshop on Learning with Limited Labeled Data    PDF

    Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
    Wenbing Huang, Mehrtash Harandi, Tong Zhang, Lijie Fan, Fuchun Sun, Junzhou Huang
    NIPS 2017   PDF / Code

    Professional Services

  • Conference Reviewer: CVPR, ICCV, ECCV, ICML, NeurIPS, AAAI, WACV

  • Misc

  • I do landscape photography, checkout my photos on Instgram.
  • I'm fond of snowboarding, especially carving and ground trick.