Bryon Aragam studies high-dimensional statistics, machine learning, and optimization. His research focuses on mathematical aspects of data science and statistical machine learning in nontraditional settings, such as heterogeneity and nonconvexity. Some of his recent projects include problems in graphical modeling, nonparametric statistics, personalization, and high-dimensional inference. He is also involved in open-source software development and problems in interpretability, ethics, and fairness in AI. His work has been published in top statistics and machine learning venues such as the Journal of Machine Learning Research, the Journal of Statistical Software, Neural Information Processing Systems, and the International Conference on Machine Learning.
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.
2019 - 2020 Course Schedule