Xiaoxiao Li

AI researcher, forever learner and traveler.


I am an Assistant Professor in the Electrical and Computer Engineering Department and an Associate Member in the Computer Science Department at the University of British Columbia (UBC). I am also faculty member at Vector Institue and an adjunct Assistant Professor at School of Medicine, Yale University.

Before joining UBC, I was a postdoc working with Prof. Kai Li and Prof. Olga Troyanskaya. I obtained my Ph.D. degree from Yale University, where I was fortunate to be advised by Prof. James Duncan. I obtained my B.S. (honors degree) from Chu Kochen College, Zhejiang University, China, in June 2015.

My current research lies in machine learning and its application to healthcare. I aim to narrow the gap between AI research and its applications by developing the next-generation trustworthy AI systems.

Please visit our group website Trusted and Efficient AI (TEA) Lab to learn about our up-to-date research projects and meet with my students.


Oct 10, 2023 Dr. Li was invited to join the Editorial Board of journal Medical Image Analysis.
Aug 28, 2023 I received Canada Foundation for Innovation Grant as a PI.
Jul 25, 2023 I received UBC Green Lab Fund as a PI.
May 1, 2023 I received New Frontiers in Research Fund - Exploration as a Co-PI with Dr. Russ Algar and Dr. Gang Wang.
Jul 27, 2022 I received Meta Research Award on Privacy Enhancing Technologie as a Co-PI.
Jul 27, 2022 I received UBC Health Innovation Funding Investment Awards with Dr. Gang Wang as a Co-PI.
May 10, 2022 I received CIFAR-Mila-IVADO grant on “Privacy-preserving medical image generation” as a Co-PI with Dr. Mi Jung Park in partner with Roche.
May 1, 2022 I received NSERC Discovery Grant as a PI on Lifelong Federated Learning.

selected publications [view all]

  1. ICML
    Federated adversarial learning: A framework with convergence analysis
    Xiaoxiao Li, Zhao Song, and Jiaming Yang
    In International Conference on Machine Learning, 2023
  2. Nature Methods
    BUDDY: molecular formula discovery via bottom-up MS/MS interrogation
    Shipei Xing, Sam Shen, Banghua Xu, Xiaoxiao Li, and Tao Huan
    Nature Methods, 2023
  3. TMI
    GATE: graph CCA for temporal SElf-supervised learning for label-efficient fMRI analysis
    Liang Peng, Nan Wang, Jie Xu, Xiaofeng Zhu, and Xiaoxiao Li
    IEEE Transactions on Medical Imaging, 2022
  4. ICLR
    FedBN: Federated learning on non-iid features via local batch normalization
    Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou
    International Conference on Learning Representations, 2021
  5. ICML
    Fl-NTK: A neural tangent kernel-based framework for federated learning analysis
    Baihe Huang, Xiaoxiao Li, Zhao Song, and Xin Yang
    In International Conference on Machine Learning, 2021
  6. MedIA
    BrainGNN: Interpretable brain graph neural network for fmri analysis
    Xiaoxiao Li, Yuan Zhou, Nicha Dvornek, Muhan Zhang, Siyuan Gao, Juntang Zhuang, Dustin Scheinost, Lawrence H Staib, Pamela Ventola, and James S Duncan
    Medical Image Analysis, 2021