Echo
AI-powered clinical trial matching, made transparent and safe.
A workflow-driven system for defining, visualizing, and executing eligibility criteria with cohort-level insight.
About this work
Echo is an AI-assisted clinical-trial-matching system developed within biomedical AI research at the Yale School of Medicine, Department of Biomedical Informatics and Data Science. The work has progressed across three iterations: the initial Cohort Visualization (2024), the Workflow Redesign (2025), and Visual Steering (2026), which is documented as an IEEE VIS 2026 short paper where I serve as co-first author. Across all three iterations I have led the interface design, the visual encoding decisions, and the frontend implementation.
Echo: Visual Steering for AI-Assisted Cohort Construction
An interactive visual analytics system for constructing, inspecting, and comparing EHR cohorts through AI-assisted SQL generation and coordinated views.
Echo: Workflow Redesign
Redesigning clinical trial matching workflows to make eligibility logic transparent and safe to execute.
Echo: Cohort Visualization
Exploring large-scale patient cohorts through interactive, data-driven visualizations.