Andrew Gelman

Andrew Gelman
Name	Andrew Gelman
Birth date	1965
Birth place	Philadelphia
Nationality	American
Fields	Statistics, Political science
Institutions	Columbia University, New York University, Stanford University
Alma mater	Massachusetts Institute of Technology, University of California, Berkeley
Doctoral advisor	Donald Rubin

Andrew Gelman

Andrew Gelman is an American statistician and political scientist known for contributions to Bayesian statistics, hierarchical modeling, and applied social science research. He is a prominent academic, public intellectual, and author whose work bridges quantitative methodology and empirical studies in political science, psychology, economics, and public policy. Gelman has influenced statistical practice through methodological innovation, pedagogy, and public-facing commentary.

Early life and education

Gelman was born in Philadelphia and raised in a family engaged with mathematics and science. He completed undergraduate studies at the Massachusetts Institute of Technology and pursued graduate education at the University of California, Berkeley, where he worked on statistical theory and applied inference. His doctoral work involved collaborations with scholars in Bayesian inference and causal analysis, and he was mentored by prominent figures such as Donald Rubin and interacted with researchers from Stanford University and Harvard University during formative training periods. During this period he developed expertise in hierarchical models and multilevel regression that would underpin later research at institutions including Columbia University and New York University.

Academic career and positions

Gelman has held faculty appointments at major research universities, most notably as professor at Columbia University and prior positions at New York University and Stanford University. He has been affiliated with interdisciplinary centers spanning political science departments, statistics departments, and public policy programs. Gelman has served as director, advisor, and visiting scholar at institutions such as the Institute for Quantitative Social Science, the American Statistical Association, and international research institutes in Canada and United Kingdom academic networks. He teaches courses linking statistical computation, Bayesian workflow, and applied regression, supervising doctoral students who have gone on to positions at Princeton University, Yale University, University of Chicago, and governmental research organizations.

Research contributions and methodology

Gelman’s methodological contributions center on Bayesian hierarchical models, multilevel regression and poststratification, model checking, and principled use of prior information. He helped advance applied Bayesian computation through work that interfaces with software developments such as Stan (software), which connects to probabilistic programming research from groups at Columbia University, University of California, Berkeley, and Princeton University. His research spans collaborations with scholars in political science, where he applied multilevel models to voting behavior and public opinion studies connected to elections like the United States presidential election, 2000 and United States presidential election, 2016. Gelman has written on causal inference, linking work with the Rubin causal model tradition and debates involving researchers at Harvard University and Yale University. He has emphasized model checking and the use of posterior predictive assessment, engaging with statisticians from University of Washington, Carnegie Mellon University, and London School of Economics. Gelman’s papers address issues in survey weighting, small-area estimation, and measurement error, drawing on collaborations with scholars from NORC at the University of Chicago, Pew Research Center, and government statistical offices.

Books and public writing

Gelman is author or coauthor of several influential texts and monographs that bridge methodology and practice, including works on Bayesian data analysis, regression and multilevel modeling, and applied statistics. His books have been used as core texts in courses at Massachusetts Institute of Technology, Columbia University, and Stanford University and are cited alongside classics from authors like Bradley Efron, Jerome Friedman, and Donald Rubin. Gelman maintains a widely read blog where he discusses statistical issues, reproducibility, public policy, and critiques of research in venues such as The New York Times, The Washington Post, and scholarly outlets including Science and Nature. He has contributed opinion pieces and technical commentaries that intersect with coverage by outlets like NPR and The New Yorker and has engaged in public debates with scholars from Oxford University, Cambridge University, and Princeton University.

Professional service and awards

Gelman has served editorial roles for leading journals in statistics and social science, including positions at publications such as Journal of the American Statistical Association, Annals of Applied Statistics, and Political Analysis. He has been active in professional societies including the American Statistical Association and the Royal Statistical Society, contributing to committees on statistical practice and replication standards. His honors include fellowship elections and recognitions from organizations such as the American Association for the Advancement of Science, university teaching awards from institutions like Columbia University, and prizes acknowledging methodological contributions that align with awards given by bodies including the Institute of Mathematical Statistics.

Personal life and advocacy

Gelman is married and has collaborated with family members and colleagues on research projects and public engagement. He is an advocate for transparent research practices, reproducibility, and statistical education reform, working with initiatives affiliated with Center for Open Science, National Institutes of Health, and professional groups in Europe and North America to promote open data and improved peer review. He frequently speaks at conferences including meetings of the American Political Science Association, the Joint Statistical Meetings, and international workshops hosted by universities such as UCL and ETH Zurich.

Category:American statisticians Category:Bayesian statisticians