The computational statistics group develops cutting edge computational methods for statistical inference, with particular strengths in Monte Carlo techniques and Bayesian nonparametrics.

Faculty

Chris Holmes

Chris Holmes

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

Post-docs

Louis Aslett

Louis Aslett

Encrypted statistical methods, parallel MCMC methods, high performance computing, reliability theory

Luke Kelly

Luke Kelly

Statistical methods for intractable models

Graduate Students

Frauke Harms

Frauke Harms

Bayesian nonparametrics, machine learning, stochastic geometry

Thibaut Lienart

Thibaut Lienart

Inference on graphical models, expectation propagation, particle methods

Xiaoyu Lu

Xiaoyu Lu

Machine learning, reinforcement learning, stochastic processes

Simon Lyddon

Simon Lyddon

Bayesian statistics, decision theory, computational statistics, machine learning.

Chris J. Maddison

Chris J. Maddison

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

Kaspar Märtens

Kaspar Märtens

Computational statistics, Bayesian machine learning, multi-view learning

Sebastian Schmon

Sebastian Schmon

Bayesian Statistics, Computational Statistics, Markov chain Monte Carlo

Stefan Webb

Stefan Webb

deep learning, deep generative models, probabilistic inference

Matthew Willetts

Matthew Willetts

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