Methods, models, and clinical evaluations underwriting the platform.
Our research advances medical AI for healthcare — foundation models that learn from real patient data and are evaluated on real clinical endpoints. Published work from our team spans world models for longitudinal EHR, whole-genome encoders, molecular LLMs, and high-resolution vision–language models.
-
2026
Research / World Model / Longitudinal EHR
The patient is not a moving document: a world-model training paradigm for longitudinal EHR
Introduces SMB-Structure, a world model for structured EHR that combines a Joint-Embedding Predictive Architecture (JEPA) with supervised next-token prediction. SFT grounds the model to reconstruct future patient states in token space; JEPA predicts those futures in latent space from the initial representation alone, forcing trajectory dynamics to be encoded before the next state is observed. Validated across 40,000 patients on long-horizon prediction tasks.
-
2025
Research / Oncology & Whole-Genome Sequencing
GenVarFormer: Predicting gene expression from long-range mutations in cancer
A whole-genome-sequencing foundation model trained to predict the functional consequence of variants on gene expression. Distinguishes rare driver mutations from passenger mutations in the non-coding genome. State-of-the-art on downstream cancer tasks.
-
2025
Research / Molecular Language Model
Patient-specific biomolecular instruction tuning of Graph-LLMs
Links proteomic graph neural networks to language, creating a shared representation space between molecular and cellular foundation models. Approach generalizes to any graph-based representation at the cellular level.
-
2025
Research / Electronic Health Records
Building the EHR foundation model via next-event prediction
Reframes EHRs as timestamped chains of clinical events and fine-tunes large language models to predict the next event, improving temporal reasoning over disease trajectories. +4.6% AUROC over task-specific EHR models.
-
2024
Research / Vision–Language / Medical Imaging
Advancing high-resolution vision–language models in biomedicine
Foundational paper showcasing the strength of the Standard Model approach across high-resolution biomedical imagery and language. Establishes the vision–language backbone that later scale-specific papers build on.