Xiaoxiao Li

AI researcher, forever learner and traveler.


3110-2332 Main Mall

Vancouver, BC V6T 1Z4

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. I am honored to be named as a Canada CIFAR AI Chair.

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.


May 20, 2024 Dr. Li is serving as an Area Chair at NeurIPS 2024.
Feb 26, 2024 Dr. Li was invited to give talks at AAAI deployable AI workshop and at FDA.
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.

selected publications [view all]

  1. CVPR
    Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
    Wenlong Deng, Christos Thrampoulidis, and Xiaoxiao Li
    Conference on Computer Vision and Pattern Recognition, 2024
  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