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

Faculty

Geoff Nicholls

Geoff Nicholls

Statistical modeling, Bayes Methods, Monte Carlo Methods.

Post-docs

M. Azim Ansari

M. Azim Ansari

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

Luke Kelly

Luke Kelly

Statistical methods for intractable models

Juho Lee

Juho Lee

Bayesian nonparametric models, random graphs

George Nicholson

George Nicholson

Computational biostatistics, machine learning, precision medicine

Tom Rainforth

Tom Rainforth

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

Andrew Roth

Andrew Roth

Computational statistics, Machine learning, Genomics, Cancer evolution

Graduate Students

Anthony Caterini

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Variational Inference

Ryan Christ

Ryan Christ

Genomics, Computational statistics, Network Analysis

Sam Davenport

Sam Davenport

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

Adam Foster

Adam Foster

Probabilistic inference, optimal experiment design, deep learning, 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

Zhu Li

Zhu Li

Kernel methods, Learning Theory, Fair Learning

Xiaoyu Lu

Xiaoyu Lu

Machine learning, Gaussian Process, Bayesian Optimization, Adaptive Importance Sampling

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

Statistical machine learning, probabilistic inference, Gaussian Processes, multi-view learning

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

Stefan Webb

Stefan Webb

deep learning, deep generative models, probabilistic inference, neural network verification

Matthew Willetts

Matthew Willetts

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

Alumni