OxCSML : Groups : Computational Statistics

The computational statistics group develops cutting edge computational methods for statistical inference, with particular strengths in Monte Carlo techniques and Bayesian nonparametrics.

Faculty

George Deligiannidis

Computational Statistics, Monte Carlo methods

Arnaud Doucet

Computational Statistics, Monte Carlo methods

Geoff Nicholls

Statistical modeling, Bayes Methods, Monte Carlo Methods.

Tom Rainforth

Machine learning, Bayesian inference, Probabilistic programming, Deep generative models

Post-docs

M. Azim Ansari

Statistical Genetics, Evolution, Host Pathogen Interactions, Computational Biostatistics, Machine Learning, Bayesian Statistics

Emile Mathieu

Probabilistic inference, Deep learning, Generative models, Representation Learning, Geometry

George Nicholson

Computational biostatistics, machine learning, precision medicine

Graduate Students

Shahine Bouabid

Kernel Methods, Gaussian processes, Climate emulation

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Variational Inference

Sam Davenport

Gaussian Processes, fMRI data, Resampling methods, Random Field Theory

Fabian Falck

Probabilistic Deep Learning, Deep Generative Models, Causality, Applications in Health

Tyler Farghly

Learning theory, Optimisation, Monte Carlo methods

Edwin Fong

Bayesian inference under model misspecification, Bayesian nonparametrics

Adam Foster

Probabilistic machine learning, deep learning, unsupervised representation learning, optimal experimental design, probabilistic programming

Frauke Harms

combinatorics, computational complexity, Bayesian nonparametrics, machine learning, stochastic geometry

Bobby He

Machine learning, deep learning, uncertainty quantification

Desi R. Ivanova

Bayesian Inference, Statistical Machine Learning, Optimal Experimental Design

Jannik Kossen

Active Learning, Bayesian Deep Learning, Transformers

Francesca Panero

Bayesian random graphs, Bayesian nonparametrics, disclosure risk

Emilia Pompe

MCMC methods, Bayesian statistics

Tim Reichelt

Probabilistic Programming, Probabilistic Inference

Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

Jean-Francois Ton

Kernel methods, Meta-learning

Hanwen Xing

Computational methods, Bayesian inference

Schyan Zafar

Monte Carlo methods, Multivariate stochastic processes

Computational Statistics

Machine Learning

Statistical Methodology

Statistical Theory