dac guests for 2025

List of Guest Speakers at the Data Analytics Colloquium (2025)

Herbert Chang

Herbert Chang is an Assistant Professor of Quantitative Social Science, Computer Science, and Mathematics at Dartmouth College, and a Forbes Under 30 Honoree in Science (https://www.forbes.com/30-under-30/2024/science?profile=herbert-chang). His research studies how emerging technologies impact democratic behavior. He has published more than 35 peer-reviewed articles on misinformation, social networks, and the political impact of AI systems. His work has been featured in the New York Times, Washington Post, and Scientific American.

Ethan C. Busby

Ethan is an Assistant Professor of Political Science at Brigham Young University, specializing in political psychology, extremism, artificial intelligence, public opinion, racial and ethnic politics, quantitative methods, and computational social science. His research relies on various methods, using lab experiments, quasi-experiments, survey experiments, text-as-data, surveys, artificial intelligence, and large-language models. His work focuses on how democratic societies should respond to extremism, using approaches from political psychology and generative AI tools. These are deeply integrated in his work—political psychology informs my use of AI, and AI tools test theories from political psychology. More specifically, his research explores what extremism is, who people blame for extremism, how political persuasion intersects with extremism, and what encourages and discourages extremism. His research has been published in a variety of presses and academic journals, including Cambridge University Press, the Journal of Politics, Political Analysis, Political Behavior, and the Proceedings of the National Academy of the Sciences.

May Yuan

May Yuan received all her degrees in Geography: B.S. 1987 from National Taiwan University and M.S. 1992 and Ph.D. 1994 from the State University of New York at Buffalo. She is Ashbel Smith Professor of Geospatial Information Sciences (GIS) in the School of Economic, Political, and Policy Sciences at the University of Texas at Dallas (UT-Dallas). She is an elected fellow of the American Association for the Advancement of Science (AAAS), the American Association of Geographers (AAG), and the University Consortium of Geographic Information Science (UCGIS). She serves as the Editor-in-Chief of the International Journal of Geographical Information Science. From July 2022 to July 2025, she was on an assignment to the National Science Foundation (NSF) as a program director of Human-Environment and Geographic Science (HEGS). Her research has been supported by NSF, NASA, DoD, DHS, DOJ, DOE, NOAA, USGS, and NIST. She and her students at the Geospatial Analytics and Innovative Applications (GAIA) Lab explore ways to understand the dynamics of people, events, and places, as well as the connections among brain health, spatial behaviors, and the environment. They also investigate the learning mechanisms taken by humans or machines to conceptualize, represent, and compute geospatial processes.

Patrick Brandt

Professor of Political Science
School of Economic, Political and Policy Studies
University of Texas, Dallas
Political and computational science research employs time series analysis methods and machine learning in a variety of areas. The main time series models employed involve Bayesian statistics, multiple equation or vector autoregression models, methods for producing and evaluating the quality of forecasts, the derivation of new models for time series of counts, and modeling structural change and endogenous shifts.
The machine learning work, in-concert with computer scientists has modernized how this work is done in political science and international relations. In addition, in recent years this has moved into work on event data technology for coding conflict and cooperation events about the civil and international actors.