Bryon Aragam studies statistical machine learning, unsupervised learning (graphical models, representation learning, latent variable models, etc.), nonparametric and high-dimensional statistics, and causal inference. 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.
2021 - 2022 Course Schedule
|41000||Business Statistics||2022 (Winter)|