AI researcher, forever learner and traveler. My first name is pronounced as "Shau-Shau".
3110-2332 Main Mall
Vancouver, BC V6T 1Z4
I am an Associate 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 and a Canada Research Chair (Tier II) in Responsible AI.
Before joining UBC, I was a postdoc working with Prof. Kai Li and Prof. Olga Troyanskaya at Princeton University. 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 improving trustworthiness and efficiency in machine learning algrithms and foundation models. I am also into novel Agentic AI systems. 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.
news
Mar 1, 2025
Dr. Li is serving as an Area Chair at NeurIPS 2025.
Oct 6, 2024
Dr. Li is serving as a member of Award Committee, a panelist of AI and Healthcare debate, and delivering a keynote on foundation model fairness at MICCAI 2024.
Sep 1, 2024
Dr. Li is serving as an Area Chair at ICLR 2025.
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.
@article{yang2025gmvaluator,title={Gmvaluator: Similarity-based data valuation for generative models},author={Yang, Jiaxi and Deng, Wenglong and Liu, Benlin and Huang, Yangsibo and Zou, James and Li, Xiaoxiao},journal={International Conference on Learning Representations},year={2025},}
ICLR
Can Textual Gradient Work in Federated Learning?
Minghui Chen, Ruinan Jin, Wenlong Deng, Yuanyuan Chen, Zhi Huang, Han Yu, and Xiaoxiao Li
International Conference on Learning Representations, 2025
@article{chen2025can,title={Can Textual Gradient Work in Federated Learning?},author={Chen, Minghui and Jin, Ruinan and Deng, Wenlong and Chen, Yuanyuan and Huang, Zhi and Yu, Han and Li, Xiaoxiao},journal={International Conference on Learning Representations},year={2025},}
ICLR
S4M: S4 for multivariate time series forecasting with Missing values
Peng Jing, Meiqi Yang, Qiong Zhang, and Xiaoxiao Li
International Conference on Learning Representations, 2025
@article{peng2025s4,title={S4M: S4 for multivariate time series forecasting with Missing values},author={Jing, Peng and Yang, Meiqi and Zhang, Qiong and Li, Xiaoxiao},journal={International Conference on Learning Representations},year={2025},}
NeurIPS
FairMedFM: fairness benchmarking for medical imaging foundation models
Ruinan Jin, Zikang Xu, Yuan Zhong, Qiongsong Yao, Qi Dou, S Kevin Zhou, and Xiaoxiao Li
Advances in Neural Information Processing Systems, 2024
@article{jin2024fairmedfm,title={FairMedFM: fairness benchmarking for medical imaging foundation models},author={Jin, Ruinan and Xu, Zikang and Zhong, Yuan and Yao, Qiongsong and Dou, Qi and Zhou, S Kevin and Li, Xiaoxiao},journal={Advances in Neural Information Processing Systems},year={2024}}
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
@article{deng2024unlocking,title={Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning},author={Deng, Wenlong and Thrampoulidis, Christos and Li, Xiaoxiao},journal={Conference on Computer Vision and Pattern Recognition},year={2024},}
Nature Methods
BUDDY: molecular formula discovery via bottom-up MS/MS interrogation
Shipei Xing, Sam Shen, Banghua Xu, Xiaoxiao Li, and Tao Huan
@article{xing2023buddy,title={BUDDY: molecular formula discovery via bottom-up MS/MS interrogation},author={Xing, Shipei and Shen, Sam and Xu, Banghua and Li, Xiaoxiao and Huan, Tao},journal={Nature Methods},pages={1--10},year={2023},publisher={Nature Publishing Group US New York},}
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
@article{peng2022gate,title={GATE: graph CCA for temporal SElf-supervised learning for label-efficient fMRI analysis},author={Peng, Liang and Wang, Nan and Xu, Jie and Zhu, Xiaofeng and Li, Xiaoxiao},journal={IEEE Transactions on Medical Imaging},volume={42},number={2},pages={391--402},year={2022},publisher={IEEE},}
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
@article{li2021fedbn,title={FedBN: Federated learning on non-iid features via local batch normalization},author={Li, Xiaoxiao and Jiang, Meirui and Zhang, Xiaofei and Kamp, Michael and Dou, Qi},year={2021},journal={International Conference on Learning Representations},}
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
@inproceedings{huang2021fl,title={Fl-NTK: A neural tangent kernel-based framework for federated learning analysis},author={Huang, Baihe and Li, Xiaoxiao and Song, Zhao and Yang, Xin},booktitle={International Conference on Machine Learning},pages={4423--4434},year={2021},organization={PMLR},}
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
@article{li2021braingnn,title={BrainGNN: Interpretable brain graph neural network for fmri analysis},author={Li, Xiaoxiao and Zhou, Yuan and Dvornek, Nicha and Zhang, Muhan and Gao, Siyuan and Zhuang, Juntang and Scheinost, Dustin and Staib, Lawrence H and Ventola, Pamela and Duncan, James S},journal={Medical Image Analysis},volume={74},pages={102233},year={2021},publisher={Elsevier},}
