
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
Marco Battiston
Bayesian nonparametrics
Benjamin BloemReddy
Bayesian nonparametrics, probabilistic modeling and inference
Luke Kelly
Statistical methods for intractable models
Juho Lee
Bayesian nonparametric models, random graphs
George Nicholson
Computational biostatistics, machine learning, precision medicine
Tom Rainforth
Bayesian inference, probabilistic programming, Monte Carlo methods
Andrew Roth
Computational statistics, Machine learning, Genomics, Cancer evolution
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Ryan Christ
Genomics, Computational statistics, Network Analysis
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Adam Foster
Probabilistic inference, probabilistic programming, Bayesian nonparametrics
Frauke Harms
Bayesian nonparametrics, machine learning, stochastic geometry
Leonard Hasenclever
Large scale machine learning, probabilistic inference, deep learning
Ho Chung Leon Law
Kernel methods, machine learning
Thibaut Lienart
Inference on graphical models, expectation propagation, particle methods
Xiaoyu Lu
Machine learning, Gaussian Process, Bayesian Optimization, Adaptive Importance Sampling
Simon Lyddon
Bayesian statistics, decision theory, computational statistics, machine learning.
Chris J. Maddison
Probabilistic inference, Monte Carlo methods, neural networks, point processes
Kaspar Märtens
Statistical machine learning, probabilistic inference, Gaussian Processes, multiview learning
Emile Mathieu
Bayesian nonparametrics, probabilistic inference, deep learning
Xenia Miscouridou
Machine Learning, Deep Generative Models, Bayesian nonparametrics, Networks
Valerio Perrone
Bayesian nonparametrics, deep learning
Emilia Pompe
MCMC methods, Bayesian statistics
Dominic Richards
Optimisation, Monte Carlo Methods
Sebastian Schmon
Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, HighDimensional Statistics
Stefan Webb
deep learning, deep generative models, probabilistic inference
Matthew Willetts
Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification