Daniel Diaz

Daniel Diaz, Ph.D., holds the position of Research Assistant Professor in the Department of Public Health Sciences, specializing in Biostatistics. His research spans biostatistical methodologies, public health applications, and interdisciplinary work in artificial intelligence ethics and cosmology.

His work focuses on:

• Prevalence estimation and handling missing data using information theory
• Statistical modeling for public health challenges
• Ethical frameworks for AI development
• Cosmological fine-tuning and theoretical physics

Recent publications highlight his cross-disciplinary approach, ranging from epidemiological modeling (e.g., correcting sampling bias in disease prevalence studies) to philosophical inquiries in astrophysics and ethical AI governance. His articles appear in journals such as IEEE Transactions on Information Theory, The Astrophysical Journal Supplement Series, and PloS One, reflecting expertise in both health sciences and computational theory.