
Computational Statistics

Machine Learning

Statistical Methodology
Computational Statistics
Machine Learning
Statistical Methodology
Faculty
George Deligiannidis
Computational Statistics, Monte Carlo methods
Arnaud Doucet
Computational Statistics, Monte Carlo methods
Geoff Nicholls
Statistical modeling, Bayes Methods, Monte Carlo Methods.
Postdocs
M. Azim Ansari
Statistical Genetics, Evolution, Host Pathogen Interactions, Computational Biostatistics, Machine Learning, Bayesian Statistics
Luke Kelly
Statistical methods for intractable models
George Nicholson
Computational biostatistics, machine learning, precision medicine
Tom Rainforth
Machine learning, Bayesian inference, Probabilistic programming, Deep generative models
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Anthony Caterini
HighDimensional Statistics, Monte Carlo Methods, Variational Inference
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Adam Foster
Probabilistic machine learning, deep learning, unsupervised representation learning, optimal experimental design, probabilistic programming
Bradley GramHansen
Probabilistic Programming, Monte Carlo Methods, Variational Inference, Computational Sustainability, Quantum Information
Frauke Harms
combinatorics, computational complexity, Bayesian nonparametrics, machine learning, stochastic geometry
Ho Chung Leon Law
Kernel methods, machine learning
Zhu Li
Kernel methods, Learning Theory, Fair Learning
Chris J. Maddison
Probabilistic inference, Monte Carlo methods, neural networks, point processes
Kaspar Märtens
Statistical machine learning, probabilistic inference, deep generative models, Gaussian Processes
Emile Mathieu
Probabilistic inference, Deep learning, Generative models, Representation Learning, Geometry
Xenia Miscouridou
Statistical Machine Learning, Deep Generative Models, Bayesian Random Graphs
Emilia Pompe
MCMC methods, Bayesian statistics
Dominic Richards
Optimisation, Monte Carlo Methods
Tim G. J. Rudner
Probabilistic inference, reinforcement learning, Gaussian Processes
Sebastian Schmon
Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, HighDimensional Statistics
JeanFrancois Ton
Kernel methods, Metalearning
Matthew Willetts
Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification
Yuan Zhou
Probabilistic programming, Machine learning, Bayesian inference