Emory University

Intracortical speech brain-to-text decoder jointly pretrained across six BCI users, with over 50% lower relative word error than per-user models.

Foundation model for paired ECG Lead-II and PPG waveforms, fusing the two through cross-modal attention and adaptive residual vector quantization.

Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.

Chemical language model that generates matched molecular pair transformations from SMILES and SMARTS to design medicinal-chemistry analogs.

Retrieval-augmented model for matched molecular pair transformations, proposing localized analog edits guided by retrieved reference compounds.

Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.

3D segmentation foundation model for female genito-pelvic anatomy, reading T2-weighted MRI and radiotherapy planning CT with one shared encoder.

Medical vision-language model trained with reinforcement learning for generalizable reasoning across eight imaging modalities and five question types.

Conditional diffusion model with cross-attention that synthesizes subject-specific 3D intrinsic connectivity networks from resting-state fMRI.

Reference-free enzyme class annotation of single unassembled metagenomic reads, assigning top-level EC classes without assembly or homology search.

DNA language model for unassembled metagenomic reads, pretrained to recover coding fraction and reading frame from 60-300 bp fragments.

Histopathology patch encoder turning 512x512 tiles into 768-dimensional features, trained with a CoCa objective on 1.26 million captioned images.

Spatiotemporal vision transformer that turns a resting-state fMRI scan into 4D brain network maps, supervised by windowed ICA components.

Convolutional ECG foundation model trained on expert annotations spanning 150 diagnostic categories, with 12-lead and single-lead wearable variants.

Foundation model for photoplethysmography (PPG) that learns quality-robust waveform representations for heart rate, blood pressure, and AF detection.