World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
32
Citations
7264
World Ranking
9506
National Ranking
2681

Overview

Giles Hooker is affiliated with the University of Pennsylvania in the United States. Their research contributions span multiple areas in computer science, with a strong focus on statistical methods, machine learning, and ecology-related studies.

Their publication record includes work in the intersection of artificial intelligence and ecological modeling, emphasizing interpretability and inference in complex data environments. Hooker has contributed extensively to the field of computer science, with a particular focus on:

  • Artificial Intelligence
  • Statistics and Probability
  • Nature and Landscape Conservation
  • Ecology
  • Ecology, Evolution, Behavior and Systematics

Main research topics covered in Hooker's work include:

  • Statistical Methods and Inference
  • Ecology and Vegetation Dynamics Studies
  • Explainable Artificial Intelligence (XAI)
  • Machine Learning and Data Classification
  • Bayesian Modeling and Causal Inference
  • Adversarial Robustness in Machine Learning
  • Animal Ecology and Behavior Studies

Their recent papers illustrate a diverse research portfolio. These include:

  • "A practical guide to selecting models for exploration, inference, and prediction in ecology," published in 2021 in Ecology
  • "Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance," 2021, Statistics and Computing
  • "A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)," 2020, Remote Sensing of Environment
  • "PCA of waveforms and functional PCA: A primer for biomechanics," 2020, Journal of Biomechanics
  • "Unbiased Measurement of Feature Importance in Tree-Based Methods," 2021, ACM Transactions on Knowledge Discovery from Data

Hooker has collaborated frequently with several researchers, including:

  • Stephen P. Ellner
  • Peter B. Adler
  • Robin E. Snyder
  • Jeremy Goldwasser
  • Christina M. Hernandez

Their work is published predominantly in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Ecology
  • Statistics and Computing
  • Machine Learning

Best Publications

  • Parameter estimation for differential equations: a generalized smoothing approach

    J. O. Ramsay;G. Hooker;D. Campbell;J. Cao

  • Functional Data Analysis with R and MATLAB

    J. O. Ramsay;Giles Hooker;Spencer Graves

  • Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods.

    Michael R. Shirts;Eric Bair;Giles Hooker;Vijay S. Pande

  • Accurate intelligible models with pairwise interactions

    Yin Lou;Rich Caruana;Johannes Gehrke;Giles Hooker

  • Data-intensive Science: A New Paradigm for Biodiversity Studies

    Steve Kelling;Wesley M. Hochachka;Daniel Fink;Mirek Riedewald

  • A practical guide to selecting models for exploration, inference, and prediction in ecology.

    Andrew T. Tredennick;Giles Hooker;Stephen P. Ellner;Peter B. Adler

  • Generalized Functional ANOVA Diagnostics for High-Dimensional Functions of Dependent Variables

    Giles Hooker

  • Quantifying uncertainty in random forests via confidence intervals and hypothesis tests

    Lucas Mentch;Giles Hooker

  • An expanded modern coexistence theory for empirical applications

    Stephen P. Ellner;Robin E. Snyder;Peter B. Adler;Giles J. Hooker

  • Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance

    Giles Hooker;Lucas Mentch;Siyu Zhou

  • Functional Generalized Additive Models

    Matthew W. McLean;Giles Hooker;Ana-Maria Staicu;Fabian Scheipl

  • Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

    Sarah Tan;Rich Caruana;Giles Hooker;Yin Lou

  • Discovering additive structure in black box functions

    Giles Hooker

  • Please Stop Permuting Features: An Explanation and Alternatives.

    Giles Hooker;Lucas Mentch

  • The Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD): A Screening Tool for Depression

    Robert D. Gibbons;Giles Hooker;Matthew D. Finkelman;David J. Weiss

  • A newly discovered role of evolution in previously published consumer-resource dynamics.

    Teppo Hiltunen;Teppo Hiltunen;Nelson G. Hairston;Nelson G. Hairston;Giles Hooker;Laura E. Jones

  • Learning Global Additive Explanations for Neural Nets Using Model Distillation

    Sarah Tan;Rich Caruana;Giles Hooker;Paul Koch

  • A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)

    J. Wen;P. Köhler;G. Duveiller;N.C. Parazoo

  • Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario.

    Giles Hooker;Stephen P. Ellner;Laura De Vargas Roditi;David J. D. Earn

  • S-LIME: Stabilized-LIME for Model Explanation

    Zhengze Zhou;Giles Hooker;Fei Wang

  • Interpreting Models via Single Tree Approximation

    Yichen Zhou;Giles Hooker

  • PCA of waveforms and functional PCA: A primer for biomechanics.

    John Warmenhoven;John Warmenhoven;Norma Bargary;Dominik Liebl;Andrew Harrison

  • Truncated linear models for functional data

    Peter Hall;Giles Hooker

  • Bayesian model robustness via disparities

    Giles Hooker;Anand N. Vidyashankar

  • Eco-Evolutionary Dynamics in a Three-Species Food Web with Intraguild Predation: Intriguingly Complex

    Teppo Hiltunen;Stephen P. Ellner;Giles Hooker;Laura E. Jones

  • Forcing function diagnostics for nonlinear dynamics.

    Giles Hooker

  • Transparent Model Distillation.

    Sarah Tan;Rich Caruana;Giles Hooker;Albert Gordo

  • Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate

    Indrayudh Ghosal;Giles Hooker

  • Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models

    Benjamin Lengerich;Sarah Tan;Chun-Hao Chang;Giles Hooker

Frequent Co-Authors

Stephen P. Ellner
Stephen P. Ellner Cornell University
James O. Ramsay
James O. Ramsay McGill University
Rich Caruana
Rich Caruana Microsoft (United States)
Peter B. Adler
Peter B. Adler Utah State University
Wesley M. Hochachka
Wesley M. Hochachka Cornell University
David W. Winkler
David W. Winkler Cornell University
Nelson G. Hairston
Nelson G. Hairston Cornell University
David Ruppert
David Ruppert Cornell University
Paul A. Pilkonis
Paul A. Pilkonis University of Pittsburgh
Ellen Frank
Ellen Frank University of Pittsburgh

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