
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
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
Machine learning, Bayesian inference, Probabilistic programming, Deep generative models
Andrew Roth
Computational statistics, Machine learning, Genomics, Cancer evolution
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Anthony Caterini
HighDimensional Statistics, Monte Carlo Methods, Variational Inference
Ryan Christ
Genomics, Computational statistics, Network Analysis
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Adam Foster
Probabilistic inference, optimal experiment design, deep learning, 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
Leonard Hasenclever
Large scale machine learning, probabilistic inference, deep learning
Ho Chung Leon Law
Kernel methods, machine learning
Zhu Li
Kernel methods, Learning Theory, Fair Learning
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
Probabilistic inference, Deep learning, Generative models, Representation Learning, Geometry
Xenia Miscouridou
Statistical Machine Learning, Deep Generative Models, Bayesian Random Graphs
Valerio Perrone
Bayesian nonparametrics, deep learning
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
Stefan Webb
deep learning, deep generative models, probabilistic inference, neural network verification
Matthew Willetts
Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification