We are a diverse group of researchers spanning many interests across machine learning, computational statistics and statistical methodology. There are ten faculty members spread over three overlapping subgroups.

Faculty

François Caron

Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis

George Deligiannidis

Computational Statistics, Monte Carlo methods

Arnaud Doucet

Computational Statistics, Monte Carlo methods

Robin Evans

Graphical models, causality, algebraic statistics

Chris Holmes

Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification

Geoff Nicholls

Statistical modeling, Bayes Methods, Monte Carlo Methods.

Tom Rainforth

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

Patrick Rebeschini

Learning theory, Optimization, Implicit Regularization

Judith Rousseau

Bayesian statistics, Asymptotics, Nonparametric statistics

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

Affiliated Faculty

Sarah Filippi

Statistical machine learning and Bayesian statistics motivated by applications in biomedicine

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

Moustafa Abdalla

Multi-view Learning, Time-series modelling, Statistical Genetics, Drug Development, High-throughput screening

Freddie Bickford Smith

Deep Learning, Uncertainty Estimation

Shahine Bouabid

Kernel Methods, Gaussian processes, Climate emulation

Christian Carmona Perez

Methods for Bayesian modeling under mispecification

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Variational Inference

Sam Davenport

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

Emilien Dupont

Deep Learning, Generative Models

Fabian Falck

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

Tyler Farghly

Learning theory, Optimisation, Monte Carlo methods

Jake Fawkes

Causal Inference, Machine Learning, Fairness

Edwin Fong

Bayesian inference under model misspecification, Bayesian nonparametrics

Adam Foster

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

Adam Goliński

Probabilistic Inference, Probablistic Programming

Aidan N. Gomez

Neural networks and deep learning

Frauke Harms

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

Bobby He

Machine learning, deep learning, uncertainty quantification

Zhiyuan Hu

Single-Cell Analysis, Ovarian Cancer, Genomics

Robert Hu

Machine Learning, Kernel Methods, Causal Inference

Desi R. Ivanova

Bayesian Inference, Statistical Machine Learning, Optimal Experimental Design

Jannik Kossen

Active Learning, Bayesian Deep Learning, Transformers

Charline Le Lan

Probabilistic Inference, Deep Learning, Reinforcement Learning

Cong Lu

Deep Reinforcement Learning, Meta-Learning, Bayesian Optimisation

Ning Miao

Deep generative models

Cian Naik

Bayesian nonparametrics, Statistical Network Analysis

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

Yuyang Shi

Statistical Machine Learning, Deep Learning, Generative Models

Jean-Francois Ton

Kernel methods, Meta-learning

Hanwen Xing

Computational methods, Bayesian inference

Jin Xu

Meta-learning, equivariance in deep learning

Schyan Zafar

Monte Carlo methods, Multivariate stochastic processes

Sheheryar Zaidi

Statistical Machine Learning, Deep Learning

Faculty

Affiliated Faculty

Post-docs

Graduate Students

Former Members

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