Samuel Kakraba, PhD Named CAIDS Senior Advisor for Health Sciences Engagement

The Connolly Alexander Institute for Data Science (CAIDS) has named Samuel Kakraba, PhD, assistant professor of biostatistics and data science at Tulane University’s Celia Scott Weatherhead School of Public Health and Tropical Medicine (WSPH), as its Senior Advisor for Health Sciences Engagement.

Beginning July 1, 2026, Kakraba will serve a two-year term in the new role alongside his existing faculty appointment. In this role, he will help strengthen connections between CAIDS and Tulane’s downtown health sciences community, including WSPH, the School of Medicine, the School of Social Work, and the Office of Research.

Kakraba’s work sits at the intersection of biostatistics, artificial intelligence, machine learning, public health, biomedical research, and drug discovery. His research focuses on developing rigorous, interpretable, scalable, and equitable AI and machine-learning workflows to support disease prediction, public health surveillance, clinical decision-making, and biomedical discovery.

The new role builds on Kakraba’s work connecting data science and AI with health research, education, and student training at Tulane. Since joining the university, he has contributed to data science and AI initiatives within WSPH, including serving on the AI Literacy Committee and the Dean’s Data Science/Artificial Intelligence Initiative. He also directs the Graduate Biostatistics Certificate Program and has developed graduate courses in Artificial Intelligence for Biomedical and Public Health Applications and Responsible AI Ethics and Governance.

Kakraba will help faculty and trainees across the downtown health sciences campus identify opportunities to use data science, AI, and machine learning in public health, clinical, biomedical, and social-health research. He will also help connect researchers and students with CAIDS programming, expertise, and resources.

Through the Senior Advisor for Health Sciences Engagement role, CAIDS aims to expand interdisciplinary collaboration across Tulane and create new pathways for researchers and students to engage with data science and AI.