Giles Hooker is interested in the interfaces of statistical inference with models both from machine learning, and from applied mathematics. This includes uncertainty quantification in random forests, and ordinary differential equations models. Hooker also works in functional data analysis and in robust statistics. Much of his work is inspired by applications, particularly in ecology, epidemiology, environmental sciences, and medicine.
Prior to Wharton, Hooker was deputy chair of the Department of Statistics at the University of California at Berkeley, and professor of statistics and data science and professor of computational biology at Cornell University. He received his MS and PhD in statistics from Stanford University.
