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

François Caron

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

Chris Holmes

Chris Holmes

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

Tom Rainforth

Tom Rainforth

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

Dino Sejdinovic

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

Yee Whye Teh

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

Post-docs

Emile Mathieu

Emile Mathieu

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

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

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, probabilistic inference, optimisation

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

Charline Le Lan

Charline Le Lan

Probabilistic Inference, Deep Learning, Reinforcement Learning

Zhu Li

Zhu Li

Kernel methods, Learning Theory, Fair Learning

Cong Lu

Cong Lu

Deep Reinforcement Learning, Meta-Learning, Bayesian Optimisation

Francesca Panero

Francesca Panero

Bayesian random graphs, Bayesian nonparametrics, disclosure risk

Tim G. J. Rudner

Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

Matthew Willetts

Matthew Willetts

deep generative models, VAEs, deep learning, variational inference, Bayesian methods

Jin Xu

Jin Xu

Meta-learning, deep generative models, representation learning, probabilistic inference

Alumni