Biography

Kennedy is a doctoral candidate at the University of Washington Information School, focused on advancing AI-ready health systems. Drawing on data science, machine learning, and health informatics, his research centers on data strategies and technologies that enable equitable integration of AI in healthcare. His dissertation is dedicated to reducing preventable blindness through inclusive, population-level AI innovation.

Research Projects
Building AI-Ready Health Systems for Equitable Diabetic Retinopathy Screening
Supported by the Population Health Initiative, this research focuses on advancing AI-enabled eye screening systems that perform reliably and equitably across diverse clinical contexts. The project addresses a critical gap in current AI screening technologies: models that perform well in narrowly validated environments often fail to generalize when deployed in new care settings.

The project has the following aims:
· Investigate methods to improve the generalizability of AI screening systems across populations, imaging devices, and care settings.
· Foster a sustainable innovation ecosystem in which researchers, clinicians, and industry partners collaborate to responsibly share data, strengthen the real-world robustness of AI screening models, and support clinics in adopting these technologies at scale.