Bryon Aragam is an Assistant Professor in the Booth School of Business at the University of Chicago. His research interests include statistical machine learning, unsupervised learning (graphical models, representation learning, latent variable models, etc.), nonparametric statistics, and causal inference. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow.