Ben Seiler is a PhD student in Statistics at Stanford University. He earned an MS in Statistics from Stanford University and 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. His research focuses on variable importance in interpretable machine learning and algorithmic fairness. He is advised by Art B. Owen.