About me

I am a research scientist in the User Modeling & Personalization team at Snap Research. My primary focuses are recommender systems and user modeling, leveraging techniques including but not limited to graph machine learning, sequential modeling, natural language processing, etc. I received a Ph.D. degree in Computer Science and Engineering from the University of Notre Dame in 2024, advised by Dr. Fanny Ye. Before that, I received my B.S. and M.S. from Case Western Reserve University, supervised by Dr. Soumya Ray.

I am actively seeking for talented Ph.D. students to do research related to recommendation systems and generative IR. If you are intereted in those topics and would like to collaborate with me, feel free to email me :). Our team at Snap Research also has multiple openings for reseasrch interns for 2025.

News

[2025.01] Two papers about RecSys are accepted to WWW 2025 and selected as oral presentations. One studies the learning dynamic of RecSys models from the perspective of matrix rank to accelerate the training process; the other studies RecSys model parameter reduction using graph-based hashing methods. Congrats to Donald and Xinyi!

[2024.11] I defended my Ph.D. dissertation! Thanks for the supports from my advisor and committe members Profs. Fanny Ye, Nitesh Chawla, Walter Scheirer, and Xiangliang Zhang.

[2024.09] One first-authored paper that studies message passing for collaborative filtering has been accepted to NeurIPS'24. See you in Vancouver.

[2024.08] Part of my previous work on multi-task self-suerpvised graph learning has been productionized for EBR retrieval of friend recommendation. Results are published in this manuscript at RobustRecSysRecSys2024! Thanks for joint efforts from our engineers and scientists.

Preprint(s):

  • Enhancing Item Tokenization for Generative Recommendation through Self-Improvement
    R. Chen, M. Ju, N. Bui, D. Antypas, S. Cai, X. Wu, L. Neves, Z. Wang, N. Shah, T. Zhao
    arXiv [pdf]

Selected Publications

[Full List] [Google Scholar]

  • Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
    D. Loveland, X. Wu, T. Zhao, D. Koutra, N. Shah, M. Ju
    WWW 2025 [pdf]

  • How Does Message Passing Improve Collaborative Filtering?
    M. Ju, W. Shiao, Z. Guo, Y. Ye, Y. Liu, N. Shah, T. Zhao
    NeurIPS 24 [pdf] [code]

  • GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
    M. Ju, T. Zhao, W. Yu, N. Shah, Y. Ye
    NeurIPS 23 [pdf] [code]

  • Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
    M. Ju, T. Zhao, Q. Wen, W. Yu, N. Shah, Y. Ye, C. Zhang
    ICLR 23 [pdf] [code]

  • Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
    M. Ju, Y. Fan, C. Zhang, Y. Ye
    AAAI 23 [pdf] [code]

  • Grape: Knowledge Graph Enhance Passage Reader for Open-domain Question Answering
    M. Ju*, W. Yu*, T. Zhao, C. Zhang, Y. Ye
    EMNLP 22 (Findings) [pdf] [code]

  • Adaptive Kernel Graph Neural Network
    M. Ju, S. Hou, Y. Fan, J. Zhao, Y. Ye, L. Zhao
    AAAI 22 [pdf] [code]

* stands for equal contribution.

Contact

  • Snap Email: mju [at] snap [dot] com
  • ND Email (deprecated soon): mju2 [at] nd [dot] edu
  • Location: 110 110th Ave NE, Bellevue, WA