Benjamin Seiler

Dr. Seiler is an Assistant Professor of Statistics at Williams College and a former postdoctoral research fellow in the Department of Epidemiology and Population Health at Stanford School of Medicine. He specializes in developing and deploying interpretable, while still effective, statistical learning methods. As part of the Stanford Human Trafficking Data Lab (HTDL), Ben works on quantitative approaches to issues of labor trafficking and child labor in Brazil. As part of the Stanford Regulation, Evaluation, and Governance Lab (RegLab), Ben works in partnership with the US Internal Revenue Service to study the use of AI to modernize the system for tax collection. He has published innovative, peer reviewed statistical methodologies for interpretable machine learning and algorithmic fairness.