Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

David Madigan received a bachelor’s degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 100 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He recently completed a term as Editor-in-Chief of Statistical Science.

Lauren Hannah is an Assistant Professor in the Department of Statistics at Columbia University. Dr. Hannah received a Ph.D. in Operations Research and Financial Engineering from Princeton University, and an A.B. in Classics, again from Princeton University. After completing her Ph.D., Dr. Hannah completed a postdoc at Duke in the Statistical Science Department. Her interests include machine learning, Bayesian statistics, and energy applications.

Eva Ascarza is an Assistant Professor of Marketing at Columbia Business School. She is a marketing modeler who uses tools from statistics and economics to answer marketing questions. Her main research areas are customer analytics and pricing in the context of subscription businesses. She specializes in understanding and predicting changes in customer behavior, such as customer retention and usage. Another stream of her research focuses on developing statistical methodologies to be used by marketing practitioners. She received her PhD from London Business School (UK) and a MS in Economics and Finance from Universidad de Navarra (Spain).