OxCSML : Groups : Machine Learning

The Oxford statistical machine learning group is engaged in developing machine learning techniques for analysing data that are scalable, flexible and robust. The group has particular strengths in Bayesian and probabilistic methods, kernel methods and deep learning, with applications to network analysis, recommender systems, text processing, spatio-temporal modelling, genetics and genomics.

Faculty

François Caron

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

Arnaud Doucet

Computational Statistics, Monte Carlo methods

Chris Holmes

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

Tom Rainforth

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

Patrick Rebeschini

Learning theory, Optimization, Implicit Regularization

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

Post-docs

Emile Mathieu

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

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

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

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

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

Jin Xu

Meta-learning, equivariance in deep learning

Sheheryar Zaidi

Statistical Machine Learning, Deep Learning

Faculty

Post-docs

Graduate Students

Alumni