Dr. Xiaoxiao Li is an Assistant 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. Dr. 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 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.
Assistant Professor, Department of Electrical and Computer Engineering, University of British Columbia
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Safe and Trustworthy AI
- Multi-model Learning
- Optimization
- Healthcare
Highlights
- Outstanding Reviewer, ICLR (2023)
- Meta Research Award (2022)
- NVIDIA Hardware Award (2021/22)
- Student Travel Award, MICCAI (2020)
- Yale Advanced Graduate Leadership Fellowship (2018-2020)
- Merit Abstract Award, OHBM (2018)
Publications
Hypernetwork-based personalized federated learning for multi-institutional CT imaging
2023
BUDDY: molecular formula discovery via bottom-up MS/MS interrogation
2023
Federated Adversarial Learning: A Framework with Convergence Analysis
2023
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
2024
Backdoor attack and defence in federated generative adversarial network-based medical image synthesis
2023
Community-Aware Transformer for Autism Prediction in fMRI Connectome
2023
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
2023
LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening
2024
Forgettable federated linear learning with certified data removal
2023
Local Superior Soups: A Catalyst for Reducing Communication Rounds in Federated Learning with Pre-trained Model
2024
Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection
2024
A Simple and Provable Approach for Learning on Noisy Labeled Multi-modal Medical Images
2024
Advances and open challenges in federated learning with foundation models
2025
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models
2025
Gmvaluator: Similarity-based data valuation for generative models
2025
Can Textual Gradient Work in Federated Learning?
2025
S4M: S4 for multivariate time series forecasting with Missing values
2025