Andrew Gelman

Andrew Gelman
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Name	Andrew Gelman
Occupation	Statistician, Professor
Employer	Columbia University

Andrew Gelman is a prominent American statistician and professor at Columbia University, known for his work in Bayesian statistics, data analysis, and political science. He has made significant contributions to the field of statistics, collaborating with renowned researchers such as Donald Rubin and Jennifer Hill. Gelman's work has been influenced by notable statisticians, including George Box and Bradley Efron, and has been applied in various fields, including epidemiology, sociology, and economics, often in conjunction with institutions like the National Institutes of Health and the World Health Organization.

● Early Life and Education

Gelman was born in New York City and grew up in New Jersey, developing an interest in mathematics and statistics at an early age, inspired by the work of John Tukey and Frederick Mosteller. He pursued his undergraduate degree in physics at MIT, where he was exposed to the ideas of Daniel Kahneman and Amos Tversky. Gelman then moved to Harvard University to earn his Ph.D. in Statistics, working under the guidance of Donald Rubin and interacting with other prominent researchers, including Stephen Stigler and David Doniger.

● Career

Gelman began his academic career as an assistant professor at University of California, Berkeley, where he collaborated with Philip Stark and Mark Hansen. He later joined the faculty at Columbia University, becoming a full professor and serving as the director of the Applied Statistics Center. Gelman has also held visiting positions at University of Chicago, University of Michigan, and New York University, working with scholars like Robert Tibshirani and Trevor Hastie. His research has been supported by grants from organizations such as the National Science Foundation, National Institutes of Health, and the Sloan Foundation.

● Research and Publications

Gelman's research focuses on Bayesian inference, regression analysis, and survey research, often incorporating insights from social network analysis and machine learning, as developed by researchers like Yee Whye Teh and Zoubin Ghahramani. He has published numerous papers in top-tier journals, including the Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Annals of Applied Statistics, frequently collaborating with colleagues like Aki Vehtari and Jonah Gabry. Gelman has also authored several books, including Bayesian Data Analysis with John Carlin, Hal Stern, and Donald Rubin, and Teaching Statistics: A Bag of Tricks with Deborah Nolan, which have been influential in the field, much like the work of David Cox and Nancy Reid.

● Awards and Honors

Gelman has received several awards for his contributions to statistics, including the COPSS Presidents' Award from the Committee of Presidents of Statistical Societies, the Wilks Memorial Award from the American Statistical Association, and the Parzen Prize from the Seminar for Statistical Science. He is a fellow of the American Statistical Association, Institute of Mathematical Statistics, and American Association for the Advancement of Science, and has been elected to the National Academy of Sciences and the American Academy of Arts and Sciences, joining the ranks of distinguished scholars like Bradley Efron and Stephen Fienberg.

● Blogging and Public Engagement

Gelman is an active blogger, maintaining a popular blog on statistics and politics, where he engages with other researchers, including Nate Silver and Sam Wang. He has written for various media outlets, such as the New York Times, Washington Post, and The Guardian, and has appeared on television programs like The Daily Show and PBS NewsHour, discussing topics like election forecasting and polling analysis with experts like Nancy Cartwright and Henry Brady. Gelman's blog has been recognized as one of the top statistics blogs by R-bloggers and has been featured in publications like Science Magazine and Nature, highlighting his ability to communicate complex statistical ideas to a broad audience, much like Hans Rosling and Edward Tufte.