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Giles Hooker's research focuses on statistical problems in machine learning, functional data analysis, robust statistics and nonlinear differential equation models. This involves estimating parameters for such equations, diagnosing when and why equations do not fit data well and developing statistical theory to account for smooth perturbations of such systems.
In machine learning, I focus on the problem of diagnostics and understanding the prediction functions that machine learning produces. Recent work in this includes estimates for conditional density and quantile functions. I am also interested in analyzing the results of experiments in machine learning in terms of determining when and why particular methods were successful.
Lastly, I am interested in robust inference via disparity-based methods such as Hellinger distance. These techniques have been in existence for 30 years or more for iid data; part of my research focusses on adapting these to regression, mixed-effects and Bayesian frameworks.