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Bryon Aragam studies statistical machine learning, nonparametric statistics, and unsupervised learning. His current interests involve (i) Statistical aspects of latent variable models, (ii) Model selection and identification in nonparametric models, and (iii) Theory and computation for deep generative models. In particular, this work focuses on applications of artificial intelligence, including tools such as ChatGPT and DALL-E. 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.

Academic Areas

  • Econometrics and Statistics

2023 - 2024 Course Schedule

Number Course Title Quarter
41000 Business Statistics 2024 (Winter)
41600 Econometrics and Statistics Colloquium 2023 (Autumn)