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 2026)
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 )
STAT4800 - Adv Stat Computing
This course covers the underlying computational methods that both underlie modern statistical and machine learning tools, as well as explicitly computational approaches to performing statistical methods. The class will cover the basics of computer arithmetic, simulation, bootstrap, jackknife and permutation methods, numerical methods for optimization and their application to statistical estimation and machine learning, nonparametric smoothing, generating random variables, and simulation methods. The course will assume familiarity with programming in the R computing environment. By the end of the course, students should be able to design and code estimation methods for sophisticated statistical models, as well as procedures to provide uncertainty about those estimates.
STAT4800401 ( 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 )
STAT5800 - Adv Stat Computing
This course covers the underlying computational methods that both underlie modern statistical and machine learning tools, as well as explicitly computational approaches to performing statistical methods. The class will cover the basics of computer arithmetic, simulation, bootstrap, jackknife and permutation methods, numerical methods for optimization and their application to statistical estimation and machine learning, nonparametric smoothing, generating random variables, and simulation methods. The course will assume familiarity with programming in the R computing environment. By the end of the course, students should be able to design and code estimation methods for sophisticated statistical models, as well as procedures to provide uncertainty about those estimates
STAT5800401 ( 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.
STAT4800 - Adv Stat Computing
This course covers the underlying computational methods that both underlie modern statistical and machine learning tools, as well as explicitly computational approaches to performing statistical methods. The class will cover the basics of computer arithmetic, simulation, bootstrap, jackknife and permutation methods, numerical methods for optimization and their application to statistical estimation and machine learning, nonparametric smoothing, generating random variables, and simulation methods. The course will assume familiarity with programming in the R computing environment. By the end of the course, students should be able to design and code estimation methods for sophisticated statistical models, as well as procedures to provide uncertainty about those estimates.
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.
STAT5800 - Adv Stat Computing
This course covers the underlying computational methods that both underlie modern statistical and machine learning tools, as well as explicitly computational approaches to performing statistical methods. The class will cover the basics of computer arithmetic, simulation, bootstrap, jackknife and permutation methods, numerical methods for optimization and their application to statistical estimation and machine learning, nonparametric smoothing, generating random variables, and simulation methods. The course will assume familiarity with programming in the R computing environment. By the end of the course, students should be able to design and code estimation methods for sophisticated statistical models, as well as procedures to provide uncertainty about those estimates
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.
STAT9950 - Dissertation
Dissertation
STAT9999 - Independent Study
Written permission of instructor and the department course coordinator required to enroll.