World's Best Scientists 2026 revealed!
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Mathematics
UK
2026

D-Index & Metrics

Mathematics

D-Index
87
Citations
67691
World Ranking
92
National Ranking
5

Engineering and Technology

D-Index
88
Citations
67945
World Ranking
306
National Ranking
23

Research.com Recognitions

  • 2026 - Research.com Mathematics in United Kingdom Leader Award
  • 2025 - Research.com Mathematics in United Kingdom Leader Award

Overview

Arnaud Doucet is affiliated with the University of Oxford in the United Kingdom. Their research spans multiple domains within computer science and mathematics, focusing notably on artificial intelligence and statistical methodology.

The scientist has published extensively with a primary emphasis on topics including Markov Chains and Monte Carlo Methods, Gaussian Processes and Bayesian Inference, and Generative Adversarial Networks and Image Synthesis. Other notable areas of work include Bayesian Methods and Mixture Models, Statistical Methods and Inference, Model Reduction and Neural Networks, and Neural Networks and Applications.

Frequent publication venues for Arnaud Doucet include:

  • arXiv (Cornell University)
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • The Annals of Applied Probability
  • SIAM Journal on Control and Optimization
  • Biometrika

Among the more recent publications are:

  • SE(3) diffusion model with application to protein backbone generation, 2023, arXiv (Cornell University)
  • Non-Reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme, 2021, Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling, 2021, arXiv (Cornell University)
  • Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates, 2021, The Annals of Applied Probability
  • COIN++: Neural Compression Across Modalities, 2022, arXiv (Cornell University)

Arnaud Doucet collaborates frequently with several coauthors, including:

  • Valentin De Bortoli
  • George Deligiannidis
  • Yee Whye Teh
  • Arthur Gretton
  • Joe Benton

The scientist's body of work covers a wide range of subfields within computer science and mathematics, particularly:

  • Artificial Intelligence
  • Statistics and Probability
  • Computer Vision and Pattern Recognition
  • Statistical and Nonlinear Physics
  • Management Science and Operations Research

Arnaud Doucet has contributed over 130 publications in computer science and nearly 60 in mathematics, marking a substantial academic involvement in their fields of study.

Best Publications

  • Sequential Monte Carlo methods in practice

    Arnaud Doucet;Nando De Freitas;Neil Gordon;Adrian Smith

  • An Introduction to Sequential Monte Carlo Methods

    Arnaud Doucet;Nando de Freitas;Neil J. Gordon

  • On sequential Monte Carlo sampling methods for Bayesian filtering

    Arnaud Doucet;Simon Godsill;Christophe Andrieu

  • An introduction to MCMC for machine learning

    Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan

  • Particle Markov chain Monte Carlo methods

    Christophe Andrieu;Arnaud Doucet;Roman Holenstein

  • The Unscented Particle Filter

    Rudolph van der Merwe;Arnaud Doucet;Nando de Freitas;Eric A. Wan

  • A Tutorial on Particle Filtering and Smoothing: Fifteen years later

    Arnaud Doucet;Adam M. Johansen

  • Sequential Monte Carlo samplers

    Pierre Del Moral;Arnaud Doucet;Ajay Jasra

  • Editors: Sequential Monte Carlo Methods in Practice

    Arnaud Doucet;Joao F. G. De Freitas;Neil J. Gordon

  • Sequential Monte Carlo methods for multitarget filtering with random finite sets

    B.-N. Vo;S. Singh;A. Doucet

  • Rao-blackwellised particle filtering for dynamic Bayesian networks

    Arnaud Doucet;Nando de Freitas;Kevin P. Murphy;Stuart J. Russell

  • A survey of convergence results on particle filtering methods for practitioners

    D. Crisan;A. Doucet

  • Particle filters for state estimation of jump Markov linear systems

    A. Doucet;N.J. Gordon;V. Krishnamurthy

  • On sequential simulation-based methods for Bayesian filtering

    Arnaud Doucet;Simon J. Godsill;Christophe Andrieu

  • Monte Carlo Smoothing for Nonlinear Time Series

    Simon J Godsill;Arnaud Doucet;Mike West

  • On Particle Methods for Parameter Estimation in State-Space Models

    Nikolas Kantas;Arnaud Doucet;Sumeetpal S. Singh;Jan Maciejowski

  • Fast Computation of Wasserstein Barycenters

    Marco Cuturi;Arnaud Doucet

  • An adaptive sequential Monte Carlo method for approximate Bayesian computation

    Pierre Moral;Arnaud Doucet;Ajay Jasra

  • Sequential monte carlo implementation of the phd filter for multi-target tracking

    Ba-Ngu Vo;S. Singh;A. Doucet

  • Particle methods for change detection, system identification, and control

    C. Andrieu;A. Doucet;S.S. Singh;V.B. Tadic

  • Augmented Neural ODEs

    Emilien Dupont;Arnaud Doucet;Yee Whye Teh

  • Sequential monte carlo samplers

    Pierre Del Moral;Arnaud Doucet

Frequent Co-Authors

Christophe Andrieu
Christophe Andrieu University of Bristol
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Simon J. Godsill
Simon J. Godsill University of Cambridge
Yee Whye Teh
Yee Whye Teh University of Oxford
Ba-Ngu Vo
Ba-Ngu Vo Curtin University
Christian P. Robert
Christian P. Robert Paris Dauphine University
Frank Wood
Frank Wood University of British Columbia
Robin J. Evans
Robin J. Evans University of Melbourne
Michael West
Michael West Duke University
Vikram Krishnamurthy
Vikram Krishnamurthy Cornell University

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