Giles Hooker

Giles Hooker
  • Professor of Statistics and Data Science

Contact Information

  • 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

Teaching

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.

In the News

Knowledge at Wharton

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).
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In the News

When AI Transparency Backfires

New research shows that AI and machine learning models can be made to look fair and neutral in their interpretability outputs while continuing to produce biased real-world decisions.…Read More

Knowledge at Wharton - 5/5/2026
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