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

• [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.
• [2024.03] I joined Snap Research as a research scientist.
• [2023.09] One first-authored paper about test-time augmentation for GNNs has been accepted to NeurIPS’23. See you in New Orleans.
• [2023.01] Three papers are accepted to ICLR’23! One first-authored one studies multi-task self-suerpvised graph learning. The others study large language models for QA and graph adversarial learning. Congrats to everyone involved!

Preprint(s):

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

Selected Publications

[Full List] [Google Scholar]

• 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: mju2 [at] nd [dot] edu
• Location: 110 110th Ave NE, Bellevue, WA