Bryon Aragam studies statistical machine learning, nonparametric statistics, and unsupervised learning. His recent work focuses on applications to artificial intelligence and deep generative models, such as those that underpin recent tools such as ChatGPT, DALL-E, and GPT-4. His work attempts to understand the statistical foundations of these models and how to improve them from both practical and theoretical perspectives. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work has been published in top statistics and machine learning venues such as the Annals of Statistics, Neural Information Processing Systems, the International Conference on Machine Learning, and the Journal of Machine Learning Research.
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. Bryon has also served as a data science consultant for technology and marketing firms, where he has worked on problems in survey design and methodology, ranking, customer retention, and logistics.