Pedro Rodriquez, Arthur Spirling, and Brandon Stewart speaking on Embedding Regression: Models for Context-Specific Description and Inference.
The Hoover Institution hosts a seminar series on Using Text as Data in Policy Analysis, co-organized by Steven J. Davis and Justin Grimmer. These seminars will feature applications of natural language processing, structured human readings, and machine learning methods to text as data to examine policy issues in economics, history, national security, political science, and other fields.
Our 17th meeting features a conversation with Pedro Rodriquez, Arthur Spirling, and Brandon Stewart on Embedding Regression: Models for Context-Specific Description and Inference on Tuesday, March 14, 2023 from 9:00AM – 10:30AM PT.
Arthur Spirling is professor of politics and data science at New York University. He received bachelor's and master's degrees from the London School of Economics, and a master's degree and PhD from the University of Rochester. Spirling's research centers on quantitative methods for social science, especially those that use text as data and, more recently, deep learning and embedding representations. His work on these subjects has appeared in outlets such as the American Political Science Review, the American Journal of Political Science, the Journal of the American Statistical Association, and conference proceedings in computer science. Substantively, he is interested in the political development of institutions, especially for the United Kingdom.
Brandon Stewart is associate professor of sociology at Princeton University, where he is also affiliated with the Politics Department, the Office of Population Research, the Princeton Institute for Computational Science and Engineering, the Center for Information Technology Policy, the Center for Statistics and Machine Learning, and the Center for the Digital Humanities. He develops new quantitative statistical methods for applications across the field of computational social science. Along with Justin Grimmer and Molly Roberts, he is the author of the 2022 book Text as Data: A New Framework for Machine Learning and the Social Sciences.
Steven J. Davis is senior fellow at the Hoover Institution and professor of economics at the University of Chicago Booth School of Business. He studies business dynamics, labor markets, and public policy. He advises the U.S. Congressional Budget Office and the Federal Reserve Bank of Atlanta, co-organizes the Asian Monetary Policy Forum and is co-creator of the Economic Policy Uncertainty Indices, the Survey of Business Uncertainty, and the Survey of Working Arrangements and Attitudes.
Justin Grimmer is a senior fellow at the Hoover Institution and a professor in the Department of Political Science at Stanford University. His current research focuses on American political institutions, elections, and developing new machine-learning methods for the study of politics.
