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Corwin (Cory) Zigler joined the faculty of The University of Texas at Austin in 2018, sharing joint appointments in the Department of Statistics and Data Sciences and the Department of Women’s Health at Dell Medical School. Prior to joining UT, he was faculty in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. He currently serves as associate editor for the journals Biometrics and Biostatistics, and is heavily involved through elected positions in the Health Policy Statistics Section of the American Statistical Association. He has received research funding from NIH, EPA, and the Health Effects Institute, and his career awards include the 2010 Carolbeth Korn Prize for the most outstanding graduating student in the UCLA School of Public Health, a 2012 Young Investigator Award from the Statistics in Epidemiology Section of the American Statistical Association, and the 2019 Rothman Prize for the best paper published in Epidemiology. Dr. Zigler's research is motivated by problems in public health and epidemiology. Specific areas of statistical methods development include methods for causal inference with interference, intermediate variables (mediation analysis, principal stratification), confounding in high dimensions, model uncertainty/model averaging, treatment effect heterogeneity, spatial statistics, missing data, environmental health data science, and tools for transparent/reproducible research.
Ph.D. in Biostatistics, UCLA, 2010
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My research focuses on statistical methods for the analysis of complex observational studies, with focus on Bayesian methods and methods for causal inference when treatments or exposures of interest are not under experimental control. Key areas of focus are the intersection of causal inference methods in problems where spatially-indexed nature of data lead to complex correlation or relational patterns, settings where inference relies in part on intermediate outcomes "on the causal pathway” towards some primary endpoint, and analysis of large administrative data fraught with measurement error and high dimensional confounding. Specific statistical research topics include causal inference with interference, spatial confounding adjustment, principal stratification, mediation analysis, Bayesian propensity score methods, and Bayesian model averaging for confounding uncertainty. Most methods problems have arisen from analyses of large administrative health data such as billing claims and public health registries, or from studies of the health impacts of air pollution regulatory policies, the latter of which often involves the integration of statistical methods with computational tools from atmospheric science and engineering.
Please visit my google scholar page or download my CV for a more complete description of work.
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