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. Some of his recent projects include problems in graphical modeling, nonparametric statistics, personalization, and high-dimensional 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 such top statistics and machine learning venues as the Annals of Statistics, Neural Information Processing Systems, International Conference on Machine Learning, and Journal of Statistical Software.
Prior to joining the University of Chicago, Aragam was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his PhD in statistics and a master’s degree in applied mathematics at the University of California, Los Angeles, where he was a National Science Foundation Graduate Research Fellow. He is a Robert H. Topel Faculty Scholar at Chicago Booth.
Photo credit: John Zich