Title: Distributed and Online Methods in Quantile Regression

Abstract: Quantile regression offers a versatile framework for modeling heterogeneous effects, but modern big data present significant computational and methodological challenges. This talk will trace a line of research on distributed and online approaches for quantile regression and related problems. In this talk, I will discuss a sequence of developments in distributed and online approaches for quantile regression, drawing connections to both classical estimation strategies and more recent advances in causal inference. I will highlight key ideas, recent progress, and future directions, with an emphasis on algorithmic insights and their relevance for large-scale applications.