Multi-Modal Representation Learning
Training shared embeddings that connect labs, symptoms, medical history, and continuous lifestyle signals.
Research
Building Foundation Models for Human Physiology
We are developing multi-modal models that connect symptoms, biomarkers, clinical history, and lifestyle signals into a unified health representation. Our goal is to make prevention and care plans more precise, explainable, and personalized.
Focus
Longitudinal risk modeling
Modality
Clinical + wearable + behavior
Active Research Pillars
Built around a physiology-first roadmap designed for high-signal, clinically relevant outputs.
Physiology-Aware Prediction
Modeling individual response trajectories to anticipate deterioration, recovery windows, and intervention impact.
Clinical Reasoning Interfaces
Designing human-in-the-loop workflows so recommendations are interpretable and grounded in evidence.
Program Pipeline
Our research stack is built to move from signal aggregation to actionable guidance while preserving rigor at every stage.
Phase 1
Curate and Normalize
Harmonize fragmented records, wearable feeds, and patient-reported context into longitudinal patient views.
Phase 2
Train and Stress-Test
Run model training with counterfactual checks, subgroup analysis, and robustness evaluations under data shift.
Phase 3
Clinical Validation
Evaluate performance with expert review protocols focused on relevance, safety, and actionability.
Phase 4
Deployment Gating
Roll out capabilities in controlled stages with traceability, monitoring, and continuous post-launch audits.
Evidence Standards
We prioritize trustworthiness before scale: transparent evaluation criteria, review loops with clinical experts, and conservative safety gates.
Protocol-Driven Evaluation
Each initiative follows predefined evaluation criteria before moving to applied workflows.
Audit-Friendly Outputs
Decisions and recommendations are logged with source context for clinical review and quality assurance.
Safety and Privacy Controls
Strict safeguards are applied for PHI handling, model access, and rollout boundaries.
Transparency Note
Research outputs are progressively released as they pass quality, safety, and interpretability milestones.