Amy Herring
Chair of the Department of Statistical Science
Sara and Charles Ayres Distinguished Professor
Professor of Statistical Science
Research Professor of Global Health
Professor of Biostatistics and Bioinformatics
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Amy Herring
Chair of the Department of Statistical Science
Sara and Charles Ayres Distinguished Professor
Professor of Statistical Science
Research Professor of Global Health
Professor of Biostatistics and Bioinformatics
Amy H. Herring is Sara & Charles Ayres Distinguished Professor of Statistical Science, Global Health, and Biostatistics and Bioinformatics at Duke University. Dr. Herring received her doctorate in biostatistics at Harvard University and came to Duke from UNC-Chapel Hill, where she was distinguished professor of biostatistics. Her research interests include development of statistical methodology for longitudinal or clustered data, Bayesian methods, latent class and latent variable models, missing data, complex environmental mixtures, and applications of statistics in population health and medicine. She has received numerous awards for her work, including the Mortimer Spiegelman Award from the American Public Health Association as the best applied public health statistician under age 40. Her research program is funded by NIH, and she holds leadership positions at the national and international level, including as Chair of the American Statistical Association's Section on Bayesian Statistical Science, as President of the International Society for Bayesian Analysis, and as a member of the Board of the International Biometric Society.
Publications
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Dombowsky A, Dunson DB, Madut DB, Rubach MP, Herring AH. BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA. Ann Appl Stat. 2025 Sep;19(3):2193–217.Nguyen PH, Engel SM, Herring AH. Prenatal Phthalate Exposures and Adiposity Outcomes Trajectories: A Multivariate Bayesian Factor Regression Approach. International journal of environmental research and public health. 2025 Sep;22(10):1466.Palmer G, Herring AH, Dunson DB. LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES. The annals of applied statistics. 2025 Mar;19(1):769–97.Petersen JM, Kahrs JC, Adrien N, Wood ME, Olshan AF, Smith LH, et al. Bias analyses to investigate the impact of differential participation: Application to a birth defects case-control study. Paediatric and perinatal epidemiology. 2024 Aug;38(6):535–43.
See more publications at Scholars@Duke


