Xiaoxiao Li
About
Appointed Canada CIFAR AI Chair – 2024
Xiaoxiao Li is an Associate Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), leading the Trusted and Efficient AI (TEA) Group, and an Adjunct Assistant Professor at the School of Medicine at Yale University. Li specializes in the interdisciplinary field of deep learning and healthcare. Their primary mission is to make AI more reliable, especially when it comes to sensitive areas like healthcare.
At the TEA Group, they explore a wide range of topics from fundamental machine learning to more focused healthcare-driven AI solutions. The group delves into topics like learning from multimodal and heterogeneous data, efficient AI models, federated learning, vision-language models and creating AI models that not only perform tasks but can also be trustworthy. Some of their groundbreaking work includes AI-driven analysis of neuroimages, pathology slides, molecular and clinical notes. In essence, Dr. Li’s work is all about bridging the world of advanced machine learning with the practical needs of the healthcare industry.
Awards
- Amazon Research Award (2025)
- Google Gemini Research Award (2025)
- Outstanding Area Chair, NeurIPS (2025)
- Canada Research Chair (Tier II) (2024)
- Outstanding Reviewer Award, ICLR (2023)
- Meta Research Award (2022)
- Nvidia Academic Award (2021/22/25)
- Yale Advanced Graduate Leadership Fellowship (2018-2020)
- Merit Abstract Award, OHBM (2018)
Relevant Publications
- Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. (2021). Fedbn: Federated learning on non-iid features via local batch normalization. ICLR 2021.
- Li, X., Zhou, Y., Dvornek, N., Zhang, M., Gao, S., Zhuang, J., ... & Duncan, J. S. (2021). BrainGNN: Interpretable brain graph neural network for fmri analysis. Medical Image Analysis, 74, 102233.
- Li, X., Song, Z., & Yang, J. (2023). Federated adversarial learning: A framework with convergence analysis. ICML 2023
- Xing, S., Shen, S., Xu, B., Li, X., & Huan, T. (2023). BUDDY: molecular formula discovery via bottom-up MS/MS interrogation. Nature Methods, 1-10.
- Chen, M., Jiang, M., Dou, Q., Wang, Z., & Li, X. (2023). FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation. MICCAI 2023