My published research examines AI readiness in public institutions and explainable AI in healthcare. The question behind both is practical: what conditions make adoption responsible, understandable, and useful?
Research themes
The State AI Preparedness Project
How governance, data, workforce capability, and infrastructure shape AI readiness.
Published systematic review
How transparent AI can support more responsible decisions in high-stakes environments.
Data for communities
How analysis and visualization can make structural conditions legible.
Work redesign
How people, process, technology, and culture interact in transformation.
Published research
AI readiness · Rutgers researchThe State AI Preparedness Project
A co-authored research project examining how ready U.S. states are to integrate AI into public services.
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Peer-reviewed paper · Algorithms (2026)Explainable AI Applications in Healthcare: A Systematic Review
A co-authored systematic review of 76 studies examining explainability methods, clinical trust, evaluation, and barriers to deployment.
Read the research →From inquiry to action
Good research does not end at a publication. It can inform a decision, a strategy, a measurement approach, or a working platform. CHEDVI is an example of that translation into a product people can explore.
Questions I return to
Who can use the evidence?
Evidence matters when it is accessible, understandable, and connected to decisions.
What conditions enable responsible AI?
Governance, data quality, capability, and context shape outcomes as much as the model does.
See public-interest data in use
CHEDVI brings complex patterns into an interface people can explore.
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