Bio
Ben Seiler is a postdoctoral research fellow in the department of Epidemiology and Population Health at the Stanford School of Medicine, with Mike Baiocchi. He specializes in developing and deploying interpretable statistical learning methods. As part of the Stanford Human Trafficking Data Lab (HTDL), Ben currently works on quantitative approaches to issues of labor trafficking and child labor in Brazil in partnership with their Federal Labor Prosecution Office. As part of the Stanford Regulation, Evaluation, and Governance Lab (RegLab), Ben currently works in partnership with the US Internal Revenue Service to study the use of AI to modernize the system for tax collection. He holds a PhD in Statistics from Stanford University, where he was advised by Art B. Owen. Before Stanford, he earned a BA magna cum laude in physics, economics, and mathematics from Williams College. After completing his BA, he worked as a foreign exchange derivatives trader at Goldman Sachs from 2013 to 2018.
