Bryon Aragam

Biography

Bryon Aragam works at the interface of artificial intelligence and statistics on how intelligent systems learn to reason from data. His current interests involve statistical aspects of causal artificial intelligence, deep generative models, and representation learning. 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 (AOS, JRSSB, JASA) and machine learning (NeurIPS, ICML, COLT) venues.

The two central pillars of his work are learning and discovery from data, with an emphasis on causality, reasoning, and abstraction in intelligent systems. Although generative models for AI have shown remarkable capacities to emulate creative processes, they still lack fundamental skills long recognized as essential for genuinely autonomous intelligence. These include abstraction, logic, and causal reasoning. He is interested in how models can learn to overcome these deficiencies.

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.

Academic Areas

• Econometrics and Statistics