Foundation and Generative Models for Digital Health

We build scalable foundation models and generative models to simulate patient trajectories and create digital twins. We also develop methods for cross-modality learning and adaptation in large models. These models support forecasting, imputation, and personalised medicine across a range of digital health applications.

Publications

Yuan, Kevin, et al. "Transformers and large language models are efficient feature extractors for electronic health record studies." Communications Medicine 5.1 (2025): 83. paper, code
Li, Chenqi, et al. "BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals." arXiv preprint arXiv:2510.02276 (2025). paper
Yangyang, Xu, et al. "Cross-Subject Mind Decoding from Inaccurate Representations." Proceedings of the ieee/cvf international conference on computer vision. 2025. paper
Ghosheh, Ghadeer O., Jin Li, and Tingting Zhu. "A survey of generative adversarial networks for synthesizing structured electronic health records." ACM Computing Surveys 56.6 (2024): 1-34. paper