Casey Overby Taylor

Casey Overby Taylor, PhD, is an Associate Professor of Medicine-General Internal Medicine and Biomedical Engineering at Johns Hopkins University, with joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. She leads the Translational Informatics Research and Innovation (TIRI) group, focusing on integrating digital health technologies into clinical care and precision medicine. Her work spans biomedical informatics, genomics, and decision support systems, with a focus on improving health equity and clinical adoption of evidence-based practices.

Education:

• PhD in Biomedical Informatics, University of Washington (2011)
• MS in Biotechnology, University of Pennsylvania (2006)
• BS in Bioinformatics, University of Michigan (2004)

Research Interests: Dr. Taylor’s research combines clinical informatics, data science, and translational research to address challenges in precision medicine, genomic integration, and wearable health technologies. Key areas include:

• Designing clinical decision support systems for genomic medicine
• Optimizing postpartum care and chronic disease management
• Evaluating implementation outcomes for health technologies

Recent Publications Trends: Her recent work emphasizes AI applications in healthcare, including opioid use prediction, wearable sensor analytics, and synthetic data generation for ethical AI training. She also explores equity in telemedicine access and genomic testing adoption.

Awards:

• 2025 Presidential Early Career Award for Scientists and Engineers (PECASE)
• 2023 Johns Hopkins Catalyst Award
• 2020-2025 NHGRI Genomic Innovator Award
• 2020-2022 Microsoft Digital Investigator Fellowship

Advising & Grants: Leads interdisciplinary teams across multiple institutions, with funding from NIH, Microsoft, and the Malone Center. Current projects include developing AI tools for clinical decision-making and genomic data integration.

Labs/Teams: Director of the TIRI Lab, collaborating with the Institute for Computational Medicine and the GIM Biomedical Informatics Data Science Section.