We introduce TabDPT, a tabular foundation model capable of providing highly accurate predictions for unseen tabular datasets with no further training or hyperparameter tuning, and demonstrate scaling in both model and pre-training dataset size.

We develop BrainOmni, the first brain foundation model that generalises across heterogeneous EEG and MEG signals.

DISCOVR is a self-supervised framework for echocardiography video representation learning that integrates spatial and temporal modeling, achieving strong generalization in anomaly detection, segmentation, and LVEF prediction.