• Ronald Reagan was a statistician and other examples of learning from diverse sources of information (Presented at Montana State University, 2013)

• Choices in statistical graphics: my stories (Presented at New York Data Visualization Meetup and Chicago Data Science Meetup, 2013)

• Half-life of a social statistician (Presented at Harvard University, 2012)

• Stan: a program for Bayesian data analysis with complex models (Presented at the Computer Science and Artificial Intelligence Laboratory, MIT, 2012, and Department of Energy Applied Mathematics Meeting, Reston, Virginia, 2011)

• Causality and statistical learning (Presented at Annual Health Economics Workshop, New York, 2012, and Montana State University, 2013, and University of Michigan, 2013)

• Little Data: How traditional statistical ideas remain relevant in a big-data world (Presented at Columbia University, 2012, and Johns Hopkins University, 2013)

• Weakly informative priors (Presented at Harvard University, 2011)

• Mathematics, statistics, and political science (Presented at Joint Statistical Meetings, Miami, 2011)

• Hierarchical modeling and prior information: an example from toxicology. (Presented at a conference on Bayesian climate reconstruction, Lamont-Doherty Earth Observatory, 2011)

• Tradeoffs in information graphics. (Presented at MIT, 2012. Earlier versions presented at University of Kentucky, Iowa State University, and University of Michigan, 2010)

• Of beauty, sex, and power: Statistical challenges in estimating small effects. (Presented at Columbia University, 2013. Earlier versions presented at Ziff Brothers Investments, Columbia University, and Princeton University, 2010)

• Creating structured and flexible models: some open problems (Presented at Warwick University, Cambridge University, and NYC R Meetup, 2010. Earlier versions presented at Massachusetts Institute of Technology, 2008, University of California, Irvine, University of California, Berkeley, and University of British Columbia, 2009)

• Culture wars, voting, and polarization: divisions and unities in modern American politics (Presented at Columbia University and at the Harvard/Manchester workshop on inequality and social change, 2010. Earlier versions presented at Harvard University, 2009, University of Washington, 2009, and Sciences Po, Paris, 2009)

• La philosophie et l'experience de la statistique bayesienne (Presented at the Paris Diderot Philmath seminar, Paris, 2010)

• La polarisation politique et comment etudier ca avec la statistique (Presented at ENSAE, Paris, 2010)

• Parameterization and Bayesian modeling (Presented at the Institut Henri Poincaré, Paris, 2009, University of Chicago, 2011, and Columbia University, 2011)

• Expanded graphical models: inference, model comparison, model checking, fake-data debugging, and model understanding (Presented at AppliBugs meeting, Paris, 2009, and Joint Statistical Meetings, Miami, 2011)

• Why we (usually) don't worry about multiple comparisons (Presented at the Association for Public Policy Analysis and Management conference, Washington, D.C., 2007, and the London School of Economics, 2009)
  Slightly different version (Presented at a meeting on statistics and neuroscience, Columbia University, 2009)

• Some computational and modeling issues for hierarchical models (Presented at the International Agency for Research on Cancer, 2009)

• Improving the presentation of quantitative results in political science (Presented, with John Kastellec, at Columbia University, 2009)

• Social and political polarization, and some other topics in network analysis (Presented at a workshop in network analysis at Harvard University, 2009)

• Bayesian generalized linear models and an appropriate default prior (Presented at the useR conference, Dortmund, 2008)

• Should the Democrats move left on economic policy? (Presented at the Joint Statistical Meetings, Denver, 2008)

• Teaching statistics (Presented at the Association for Psychological Science meeting, Chicago, 2008)

• Red state, blue state, rich state, poor state: Why Americans vote the way they do (updated after the 2008 election) (Presented at the New York Young Republican Club, California Institute of Technology, Google, University of California, Berkeley, University of British Columbia, London School of Economics, 2009, and Cambridge University, 2010)

• Some recent progress in simple statistical methods (Presented at the mini-symposium on statistical consulting, Applied Statistics Center, Columbia University, 2008)

• Hierarchical modeling: a unifying framework and some open questions (Presented at the 50th Anniversary Meeting of the Department of Statistics, Harvard University, 2007)

• Culture wars, voting, and polarization: divisions and unities in modern American politics (Presented at Dartmouth College, 2007)
  Handout that goes with the talk

• Arsenic and old models (Presented for SAC Capital Management, New York, 2007)

• Weakly informative priors (Presented at the Workshop on Monte Carlo Methods, Harvard University, 2007)

• Rich state, poor state, red state, blue state: What's the matter with Connecticut? A demonstration of multilevel modeling (Presented at the Department of Economics, George Mason University, 2005, and the Institute of Statistics and Decision Sciences, Duke University, 2006)
  Updated version (Presented at the American Sociological Association meeting, Montreal, 2006, and Yale University, 2007. Other versions presented at the Cato Institute, New America Foundation, Princeton Club of New York, and Columbia University, 2008)

• Mathematical vs. statistical models in social science (Dresden lecture, Department of Mathematics, Swarthmore College, 2005)

• Coalitions, voting power, and political instability (Dresden lecture, Department of Mathematics, Swarthmore College, 2005, and in the Math Across Campus series, University of Washington, 2009)

• Interactions in multilevel models (Presented at the Joint Statistical Meetings, Minneapolis, 2005)

• Teaching statistics: a bag of tricks (Presented at Smith College, 2005). This talk was accompanied by several demonstrations and handouts, and this slideshow by itself has parts that may be hard to follow without that supplementary material.

• Some questions (and a few answers) about multilevel models (Presented at the Centers for Disease Control and Prevention, 2005)

• Learning about social and political polarization using ``How many X's do you know'' surveys (Presented at the Department of Statistics, Harvard University, 2005)
  Updated version (Presented at Oxford University, 2007)

• Ubiquity of multilevel models and how to understand them better. (Presented at the Institute for Social Research, University of Michigan, 2004, and the Department of Political Science, Stanford University, 2005)

• Fitting and understanding multilevel (hierarchical) models. (Presented at the Department of Government, Harvard University, 2004)

• Survey weighting and hierarchical regression: some successes and struggles. (Presented at the Department of Statistics, Yale University, 2004)

• Polls and Presidential elections. (Presented at the Industrial Engineering and Operations Research seminar, Columbia University, 2004)

• Toward an environment for Bayesian data analysis in R. (Presented at the Joint Statistical Meetings, Toronto, 2004)

• Computation for Bayesian data analysis. (Presented at the Joint Statistical Meetings, Toronto, 2004)

• Survey weighting and hierarchical regression. (Presented at the Joint Statistical Meetings, Toronto, 2004)

• Bayesian data analysis: what it is and what it is not (Presented at the Department of Computer Science, Columbia University, 2003)

• Combining information and group decision making. (Presented at the Workshop on Information Aggregation in Decision Making, Silver Spring, Maryland, 2003)

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