2026 Frank Wood: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	38	10354	10063	414	395	140	5218

Overview

Frank Wood is affiliated with the University of British Columbia in Canada. Their research spans a broad array of topics within computer science, with particular emphasis on artificial intelligence.

Wood's research contributions cover the following main fields of study:

Computer Science

Their subfields of study include:

Artificial Intelligence
Computer Vision and Pattern Recognition
Management Science and Operations Research
Modeling and Simulation
Statistical and Nonlinear Physics

Key topics within their work are:

Gaussian Processes and Bayesian Inference
Bayesian Modeling and Causal Inference
Machine Learning and Algorithms
Generative Adversarial Networks and Image Synthesis
Machine Learning and Data Classification
COVID-19 epidemiological studies
Advanced Bandit Algorithms Research

Recent publications by Frank Wood include the following:

"Some Formal Structures in Probability (Invited Talk)," 2021, arXiv (Cornell University)
"Enhancing Few-Shot Image Classification with Unlabelled Examples," 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
"On the Challenges and Opportunities in Generative AI," 2024, arXiv (Cornell University)
"Differentiable Particle Filtering without Modifying the Forward Pass," 2021, arXiv (Cornell University)
"Planning as Inference in Epidemiological Models," 2020, arXiv (Cornell University)

Frequent co-authors collaborating with Wood include:

Vaden Masrani
Adam Ścibior
Saeid Naderiparizi
Berend Zwartsenberg
Andrew Warrington

Wood's work is primarily disseminated through publication venues such as:

arXiv (Cornell University)
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Frontiers in Artificial Intelligence
Proceedings of the International Symposium on Combinatorial Search
2022 International Joint Conference on Neural Networks (IJCNN)

Best Publications

A New Approach to Probabilistic Programming Inference

Frank D. Wood;Jan-Willem van de Meent;Vikash Mansinghka

353
Citations
Learning Disentangled Representations with Semi-Supervised Deep Generative Models

N. Siddharth;Brooks Paige;Brooks Paige;Jan-Willem van de Meent;Alban Desmaison

278
Citations
Diagnosis code assignment: models and evaluation metrics

Adler J. Perotte;Rimma Pivovarov;Karthik Natarajan;Karthik Natarajan;Nicole Gray Weiskopf

270
Citations
On the variability of manual spike sorting

F. Wood;M.J. Black;C. Vargas-Irwin;M. Fellows

250
Citations
Improved Few-Shot Visual Classification

Peyman Bateni;Raghav Goyal;Vaden Masrani;Frank Wood

199
Citations
Deep Variational Reinforcement Learning for POMDPs

Maximilian Igl;Luisa M. Zintgraf;Tuan Anh Le;Frank Wood

175
Citations
A nonparametric Bayesian alternative to spike sorting.

Frank Wood;Michael J. Black

174
Citations
Hierarchically Supervised Latent Dirichlet Allocation

Adler J. Perotte;Frank Wood;Noemie Elhadad;Nicholas Bartlett

167
Citations
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

Sam Staton;Frank Wood;Hongseok Yang;Chris Heunen

149
Citations
An Introduction to Probabilistic Programming

Jan-Willem van de Meent;Brooks Paige;Hongseok Yang;Frank Wood

145
Citations
Tighter Variational Bounds are Not Necessarily Better

Tom Rainforth;Adam R. Kosiorek;Tuan Anh Le;Chris J. Maddison

142
Citations
A stochastic memoizer for sequence data

Frank Wood;Cédric Archambeau;Jan Gasthaus;Lancelot James

136
Citations
Auto-Encoding Sequential Monte Carlo

Tuan Anh Le;Maximilian Igl;Tom Rainforth;Tom Jin

134
Citations
Online Learning Rate Adaptation with Hypergradient Descent

Atilim Gunes Baydin;Robert Cornish;David Martinez Rubio;Mark Schmidt

128
Citations
Using synthetic data to train neural networks is model-based reasoning

Tuan Anh Le;Atilim Giines Baydin;Robert Zinkov;Frank Wood

112
Citations
A non-parametric Bayesian method for inferring hidden causes

Frank Wood;Thomas L. Griffiths;Zoubin Ghahramani

111
Citations
Inference Compilation and Universal Probabilistic Programming

Tuan Anh Le;Atilim Gunes Baydin;Frank D. Wood

110
Citations
Design and Implementation of Probabilistic Programming Language Anglican

David Tolpin;Jan-Willem van de Meent;Hongseok Yang;Frank Wood

103
Citations
Inference networks for sequential Monte Carlo in graphical models

Brooks Paige;Frank Wood

91
Citations
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

Sam Staton;Hongseok Yang;Chris Heunen;Ohad Kammar

85
Citations
The sequence memoizer

Frank Wood;Jan Gasthaus;Cédric Archambeau;Lancelot James

85
Citations
On Nesting Monte Carlo Estimators

Tom Rainforth;Robert Cornish;Hongseok Yang;Andrew Warrington

58
Citations

Frequent Co-Authors

Yee Whye Teh University of Oxford

Hongseok Yang Korea Advanced Institute of Science and Technology

Philip H. S. Torr University of Oxford

Michael J. Black Max Planck Institute for Intelligent Systems

Aram Galstyan University of Southern California

Arnaud Doucet University of Oxford

Pushmeet Kohli DeepMind (United Kingdom)

Noah D. Goodman Stanford University

Mark Schmidt University of British Columbia

John P. Donoghue Brown University

External Links

Personal website of Frank Wood Google Scholar page

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