Paul Zivich

Dr. Paul Zivich is an Assistant Professor in the Department of Epidemiology at the University of North Carolina at Chapel Hill (UNC-CH). His research focuses on causal inference methodologies, machine learning applications in epidemiology, and infectious disease prevention. He holds a PhD in Epidemiology from UNC-CH (2021), an MPH from The Ohio State University (2016), and a BSPH from the same institution (2015).

• Education:
• PhD in Epidemiology, UNC-CH Gillings School, 2021
• MPH in Public Health, Ohio State University, 2016
• BSPH in Public Health, Ohio State University, 2015

His work bridges statistical theory and public health practice, emphasizing computational tools like the delicatessen Python library for M-estimation. Research interests include non-standard causal inference approaches, positivity violations, and fusion designs for combining data sources. He has contributed to studies on HIV prevention, influenza vaccination strategies, and SARS-CoV-2 testing optimization.

Notable presentations include talks at the American Causal Inference Conference (2025), Society for Epidemiologic Research (2022–2024), and international workshops on computational epidemiology. His work promotes FAIR principles through open-source software and reproducible methods.