Machine learning; dynamical systems; computational methods in statistics; statistical applications; functional data analysis
Current Research Interest
Quantifying uncertainty in machine learning; diagnostic methods for machine learning models; statistical inference for differential equation models; applications of functional data analysis in ecology; robust statistical methods
National Science Foundation CAREER Award
Tim Matthews Memorial Fulbright Scholarship
Hall, Peter and Giles Hooker. “Truncated Linear Models for Functional Data.” Journal of the Royal Statistical Society Series B 78, no. 3 (2016): 637-653.
Mentch, Lucas and Giles Hooker. “Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests.” Journal of Machine Learning Research 17, no. 3 (2015): 1-41.
Cornell Research Website Article
Statistics, Machine Learning, Uncertainty