Abstract

"Modern data analysis increasingly requires learning not only average trends, but also heterogeneity, uncertainty, tail behavior, and how information can be fused across heterogeneous data sources. In this talk, I will discuss how the quantile regression process
provides a flexible semiparametric approach to these problems by learning conditional distributions without imposing strong parametric assumptions on their shape.

I will highlight its role in several modern statistical problems, including multiple imputation, Bayesian inference, extreme quantile analysis, and conformal prediction, where quantile processes can help construct density-based nonconformity scores and prediction regions under complex error distributions. I will also discuss rank-based data integration motivated by the fusion
of multiple epigenetic clocks for assessing biological aging. Together, these examples illustate how quantile-based thinking can move beyond mean-centered modeling toward a richer and more robust understanding of variation, uncertainty, and individualized prediction."

About the speaker

"Huixia (Judy) Wang is the William Marsh Trustee Professor in Data Science and Chair of the Statistics Department at Rice University. She previously held faculty positions at The George Washington University and North Carolina State University and served as a Program Director at the National Science Foundation from 2018 to 2022. Her research spans statistical learning, uncertainty quantification, high-dimensional inference, quantile regression, extreme value theory and applications, spatial data analysis. She is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an elected member of
the International Statistical Institute, and currently serves as Co-Editor of Statistica Sinica"