
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
Emile Mathieu
Probabilistic inference, Deep learning, Generative models, Representation Learning, Geometry
Graduate Students
Moustafa Abdalla
Multiview Learning, Timeseries modelling, Statistical Genetics, Drug Development, Highthroughput screening
Shahine Bouabid
Kernel Methods, Bayesian Nonparametrics, Deep Learning, AerosolCloud Interaction
Anthony Caterini
HighDimensional Statistics, Monte Carlo Methods, Variational Inference
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Emilien Dupont
Deep Learning, Generative Models
Fabian Falck
Probabilistic Deep Learning, Deep Generative Models, Causality, Applications in Health
Tyler Farghly
Learning theory, Optimisation, Monte Carlo methods
Jake Fawkes
Causal Inference, Machine Learning, Fairness
Edwin Fong
Bayesian inference under model misspecification, Bayesian nonparametrics
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
Frauke Harms
combinatorics, computational complexity, Bayesian nonparametrics, machine learning, stochastic geometry
Bobby He
Machine learning, deep learning, uncertainty quantification
Robert Hu
Machine Learning, Kernel Methods, Causal Inference, Deep Learning, Industrial datasets, Variational Inference, Tensor Learning
Desi R. Ivanova
Bayesian Inference, Statistical Machine Learning, Optimal Experimental Design
Jannik Kossen
Active Learning, Bayesian Deep Learning, Transformers
Charline Le Lan
Probabilistic Inference, Deep Learning, Reinforcement Learning
Cong Lu
Deep Reinforcement Learning, MetaLearning, Bayesian Optimisation
Ning Miao
Deep generative models
Francesca Panero
Bayesian random graphs, Bayesian nonparametrics, disclosure risk
Emilia Pompe
MCMC methods, Bayesian statistics
Tim Reichelt
Probabilistic Programming, Probabilistic Inference
Tim G. J. Rudner
Probabilistic inference, reinforcement learning, Gaussian Processes
Yuyang Shi
Statistical Machine Learning, Deep Learning, Generative Models
JeanFrancois Ton
Kernel methods, Metalearning
Joost van Amersfoort
Machine Learning, Variational Inference, Uncertainty Estimation
Jin Xu
Metalearning, equivariance in deep learning
Sheheryar Zaidi
Statistical Machine Learning, Deep Learning
Alumni
 Fadhel Ayed
 Marco Battiston
 Benjamin BloemReddy
 Giuseppe Di Benedetto
 Seth Flaxman
 Bradley GramHansen
 Leonard Hasenclever
 Yunlong Jiao
 Hyunjik Kim
 Adam R. Kosiorek
 Ho Chung Leon Law
 Juho Lee
 Zhu Li
 Thibaut Lienart
 Xiaoyu Lu
 Simon Lyddon
 Chris J. Maddison
 Kaspar Märtens
 Xenia Miscouridou
 Jovana Mitrovic
 Konstantina Palla
 Valerio Perrone
 Dominic Richards
 Sebastian Schmon
 Stefan Webb
 Matthew Willetts
 ChiehHsi (Jessie) Wu
 Qinyi Zhang
 Yuan Zhou