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

François Caron

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

Robin Evans

Robin Evans

Graphical models, causality, algebraic statistics

Chris Holmes

Chris Holmes

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

Geoff Nicholls

Geoff Nicholls

Statistical modeling, Bayes Methods, Monte Carlo Methods.

Tom Rainforth

Tom Rainforth

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

Judith Rousseau

Judith Rousseau

Bayesian statistics, Asymptotics, Nonparametric statistics

Dino Sejdinovic

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

Yee Whye Teh

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

Affiliated Faculty

Sarah Filippi

Sarah Filippi

Statistical machine learning and Bayesian statistics motivated by applications in biomedicine

Post-docs

M. Azim Ansari

M. Azim Ansari

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

Emile Mathieu

Emile Mathieu

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

George Nicholson

George Nicholson

Computational biostatistics, machine learning, precision medicine

Graduate Students

Moustafa Abdalla

Moustafa Abdalla

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

Shahine Bouabid

Shahine Bouabid

Kernel Methods, Bayesian Nonparametrics, Deep Learning, Aerosol-Cloud Interaction

Anthony Caterini

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Variational Inference

Sam Davenport

Sam Davenport

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

Fabian Falck

Fabian Falck

Probabilistic deep learning, Deep generative models, VAEs, Causal inference

Tyler Farghly

Tyler Farghly

Learning theory, Optimisation, Monte Carlo methods

Jake Fawkes

Jake Fawkes

Causal Inference, Machine Learning, Fairness

Edwin Fong

Edwin Fong

Bayesian inference under model misspecification, Bayesian nonparametrics

Adam Foster

Adam Foster

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

Frauke Harms

Frauke Harms

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

Bobby He

Bobby He

Machine learning, deep learning, uncertainty quantification

Zhiyuan Hu

Zhiyuan Hu

Single-Cell Analysis, Ovarian Cancer, Genomics

Robert Hu

Robert Hu

Machine Learning, Kernel Methods, Causal Inference, Deep Learning, Industrial datasets, Variational Inference, Tensor Learning

Desi R. Ivanova

Desi R. Ivanova

Bayesian Inference, Statistical Machine Learning, Optimal Experimental Design

Jannik Kossen

Jannik Kossen

Active Learning, Bayesian Deep Learning, Transformers

Charline Le Lan

Charline Le Lan

Probabilistic Inference, Deep Learning, Reinforcement Learning

Cong Lu

Cong Lu

Deep Reinforcement Learning, Meta-Learning, Bayesian Optimisation

Cian Naik

Cian Naik

Bayesian nonparametrics, Statistical Network Analysis

Francesca Panero

Francesca Panero

Bayesian random graphs, Bayesian nonparametrics, disclosure risk

Tim Reichelt

Tim Reichelt

Probabilistic Programming, Probabilistic Inference

Tim G. J. Rudner

Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

Yuyang Shi

Yuyang Shi

Statistical Machine Learning, Deep Learning, Generative Models

Hanwen Xing

Hanwen Xing

Computational methods, Bayesian inference

Jin Xu

Jin Xu

Meta-learning, equivariance in deep learning

Schyan Zafar

Schyan Zafar

Monte Carlo methods, Multivariate stochastic processes

Former Members