Giles Hooker

• 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.

Past 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.

Knowledge @ Wharton

• Why Big Companies Are Rethinking How They Work With Startups, Knowledge @ Wharton - 07/10/2026
• Can Congress Fix America’s Housing Affordability Crisis?, Knowledge @ Wharton - 07/08/2026
• Rethinking How We Measure Soccer Performance, Knowledge @ Wharton - 07/08/2026
• Why ‘AI Ethics’ Is a Misnomer — and Why That Matters for Business, Knowledge @ Wharton - 07/07/2026
• How Retailers Adapt When Growth Slows, Knowledge @ Wharton - 07/07/2026
• The Fed Explained: What It Does and Why It Matters, Knowledge @ Wharton - 07/07/2026
• Why America Leads the World in Innovation, Knowledge @ Wharton - 07/03/2026
• Piedmont Healthcare’s Marketing Strategy for Building Trust, Knowledge @ Wharton - 07/02/2026
• Navigating Competition and Collaboration in the AI Race, Knowledge @ Wharton - 07/01/2026
• Breaking Down the World Cup With Soccer Analytics Insights, Knowledge @ Wharton - 07/01/2026