Julian Bernado is a Senior Research Data Analyst for the SCALE Initiative. His research combines traditional causal inference methods to estimate treatment effects with modern techniques for analyzing text data in the age of language models.
Prior to joining SCALE, Julian developed methods for causal matching under Ben B. Hansen in collaboration with the Texas Education Agency. Before that he worked on childhood health in the Stanford School of Medicine as well as principled deployment of black-box models in the Stanford Department of Statistics. He holds a B.S. in Mathematics from Stanford and an M.A. in Statistics from the University of Michigan.