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

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., Moritz Gögl, and Tingting Zhu. "A perspective on individualized treatment effects estimation from time-series health data." Journal of the American Medical Informatics Association (2025): ocae323. 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