Hao has over two decades of experience in teaching statistics to graduate students in sociology, education, and political science.
She is widely recognized for her effectiveness in giving students a solid understanding of advanced methods combined with the ability to apply those methods to real-world problems. She coauthored the book Quantile Regression and has conducted research on social inequality applying quantile regression.
After earning a doctorate in sociology at the University of Chicago, Hao completed a postdoctoral fellowship at the Labor and Population Program of RAND. She spent several years as a professor at the University of Iowa before moving to Johns Hopkins in 1996. She has published four books and more than 50 articles on topics that include social inequality, migration, family demography, sociology of education, and quantitative and computational methods. Her work has been supported by multiple NSF and NIH grants.
Hao’s current quantitative research focuses on developing applications of advanced quantile regression modeling to 21st century data opportunities, applying network science to model the latent structure of U.S. employment relations, and constructing agent-based models to simulate social processes of network formation, norm emergence, and sanctioning.
Hao has been a Russell Sage Foundation Residential Fellow and a Spencer Foundation Residential Fellow.
You can visit her university webpage here.
In linear regression, we model the conditional mean of a dependent variable as a function of a set of predictors. In some cases, that’s all we need. But there’s actually a whole conditional distribution of the dependent variable that we...View Details