Giles Hooker

Professor of Statistics and Data Science

Contact Information

Primary Email:
ghooker@wharton.upenn.edu

office Address:
409 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104

Research Interests: Machine Learning, Functional Data Analysis, Differential Equations, Computational Statistics, Statistical Ecology

Links: Personal Website

Research

Christina Hernandez, Stephen Ellner, Robin Snyder, Giles Hooker (2024), The natural history of luck: A synthesis study of structured population models, Ecology Letters , 27 (3).
Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana (2023), Considerations when learning additive explanations for black-box models, Machine Learning, 112 (p.p. 3333-3359).
Christina Hernandez, Stephen Ellner, Peter Adler, Giles Hooker, Robin Snyder (2023), An exact version of Life Table Response Experiment analysis, and the R package exactLTRE, Methods in Ecological Forecasting, 14 (3), pp. 939-951.
Yichen Zhou, Zhengze Zhou, Giles Hooker (2023), Approximate Trees: Statistical Stability in Model Distillation, Data Mining and Knowledge Discovery , 38 (p.p. 3308-3346).
Yichen Zhou and Giles Hooker (2022), Decision tree boosted varying coefficient models, Data Mining and Knowledge Discovery , 36 (p.p. 2237-2271).
Stephen Ellner, Robin Snyder, Peter Adler, Giles Hooker (2022), Toward a “Modern Coexistence Theory” for the Discrete and Spatial, Ecological Monographs , 92 (4).
Yichen Zhou and Giles Hooker (2022), Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution, Journal of Machine Learning Research , 23 (183), pp. 1-44.
Giles Hooker and Hanlin Shang (2022), Selecting the Derivative of a Functional Covariate in Scalar-on-Function Regression, Statistics and Computing , 32 (3), pp. 35-47.
David Sinclair and Giles Hooker (2021), An Expectation Maximization Algorithm for High-Dimensional Model Selection for the Ising Model with Misclassified States, Journal of Applied Statistics , 49 (16), pp. 4049-4068.

Teaching

Current Courses (Spring 2025)

STAT4700 - Data Analy & Stat Comp
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.
STAT4700401 ( Syllabus )
STAT4700402 ( Syllabus )
STAT5030 - Data Analy & Stat Comp
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Two courses at the statistics 4000 or 5000 level.
STAT5030401 ( Syllabus )
STAT5030402 ( Syllabus )
STAT9916 - Sem In Adv Appl Of Stat
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
STAT9916301 ( Syllabus )

All Courses

STAT4700 - Data Analy & Stat Comp
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.
STAT5030 - Data Analy & Stat Comp
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Two courses at the statistics 4000 or 5000 level.
STAT9916 - Sem in Adv Appl of Stat
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

Activity

Latest Research

Christina Hernandez, Stephen Ellner, Robin Snyder, Giles Hooker (2024), The natural history of luck: A synthesis study of structured population models, Ecology Letters , 27 (3).

All Research