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

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

Yunlong Jiao

Yunlong Jiao

Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics

Tom Rainforth

Tom Rainforth

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

Graduate Students

Moustafa Abdalla

Moustafa Abdalla

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

Fadhel Ayed

Fadhel Ayed

Bayesian nonparametrics, Machine Learning

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

Adam Foster

Adam Foster

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

Bradley Gram-Hansen

Bradley Gram-Hansen

Probabilistic Programming, Monte Carlo Methods, Variational Inference, Computational Sustainability, Quantum Information

Frauke Harms

Frauke Harms

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

Robert Hu

Robert Hu

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

Charline Le Lan

Charline Le Lan

Probabilistic Inference, Deep Learning, Reinforcement Learning

Zhu Li

Zhu Li

Kernel methods, Learning Theory, Fair Learning

Chris J. Maddison

Chris J. Maddison

Probabilistic inference, Monte Carlo methods, neural networks, point processes

Kaspar Märtens

Kaspar Märtens

Statistical machine learning, probabilistic inference, deep generative models, Gaussian Processes

Emile Mathieu

Emile Mathieu

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

Xenia Miscouridou

Xenia Miscouridou

Statistical Machine Learning, Deep Generative Models, Bayesian Random Graphs

Tim G. J. Rudner

Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

Sebastian Schmon

Sebastian Schmon

Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, High-Dimensional Statistics

Matthew Willetts

Matthew Willetts

Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification

Jin Xu

Jin Xu

Probabilistic inference, deep generative models, representation learning

Qinyi Zhang

Qinyi Zhang

Statistical Machine Learning, Kernel Method, Nonparametric Association Measures

Yuan Zhou

Yuan Zhou

Probabilistic programming, Machine learning, Bayesian inference

Alumni