
Computational Statistics

Machine Learning

Statistical Methodology

Statistical Theory
Computational Statistics
Machine Learning
Statistical Methodology
Statistical Theory
Faculty
François Caron
Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis
Arnaud Doucet
Computational Statistics, Monte Carlo methods
Chris Holmes
Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification
Tom Rainforth
Machine learning, Bayesian inference, Probabilistic programming, Deep generative models
Patrick Rebeschini
Learning theory, Optimization, Implicit Regularization
Dino Sejdinovic
Statistical machine learning, kernel methods, nonparametric statistics
Yee Whye Teh
Bayesian nonparametrics, probabilistic learning, deep learning
Postdocs
Yunlong Jiao
Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Moustafa Abdalla
Multiview Learning, Timeseries modelling, Statistical Genetics, Drug Development, Highthroughput screening
Fadhel Ayed
Bayesian nonparametrics, Machine Learning
Anthony Caterini
HighDimensional Statistics, Monte Carlo Methods, Variational Inference
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Giuseppe Di Benedetto
Bayesian nonparametrics, Machine Learning
Emilien Dupont
Deep Learning, Generative Models
Adam Foster
Probabilistic machine learning, deep learning, unsupervised representation learning, optimal experimental design, probabilistic programming
Adam Goliński
Probabilistic Inference, Probablistic Programming
Aidan N. Gomez
Neural networks and deep learning
Bradley GramHansen
Probabilistic Programming, Monte Carlo Methods, Variational Inference, Computational Sustainability, Quantum Information
Frauke Harms
combinatorics, computational complexity, Bayesian nonparametrics, machine learning, stochastic geometry
Bobby He
Machine learning, probabilistic inference, optimisation
Robert Hu
Machine Learning, Kernel Methods, Causal Inference, Deep Learning, Industrial datasets, Variational Inference, Tensor Learning
Adam R. Kosiorek
Approximate Inference, Deep Generative Models
Charline Le Lan
Probabilistic Inference, Deep Learning, Reinforcement 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
Francesca Panero
Bayesian random graphs, Bayesian nonparametrics, disclosure risk
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
Joost van Amersfoort
Machine Learning, Variational Inference, Neural Networks
Matthew Willetts
Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification
Jin Xu
Metalearning, deep generative models, representation learning, probabilistic inference
Yuan Zhou
Probabilistic programming, Machine learning, Bayesian inference