We are a diverse group of researchers spanning many interests across machine learning, computational statistics and statistical methodology. There are ten faculty members spread over three overlapping subgroups.

## Faculty

### François Caron

Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis

### George Deligiannidis

Computational Statistics, Monte Carlo methods

### Arnaud Doucet

Computational Statistics, Monte Carlo methods

### Robin Evans

Graphical models, causality, algebraic statistics

### Chris Holmes

Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification

### Geoff Nicholls

Statistical modeling, Bayes Methods, Monte Carlo Methods.

### Tom Rainforth

Machine learning, Bayesian inference, Probabilistic programming, Deep generative models

### Patrick Rebeschini

Learning theory, Optimization, Implicit Regularization

### Judith Rousseau

Bayesian statistics, Asymptotics, Nonparametric statistics

### Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

### Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

## Affiliated Faculty

### Sarah Filippi

Statistical machine learning and Bayesian statistics motivated by applications in biomedicine

## Post-docs

### M. Azim Ansari

Statistical Genetics, Evolution, Host Pathogen Interactions, Computational Biostatistics, Machine Learning, Bayesian Statistics

### Yunlong Jiao

Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics

### Luke Kelly

Statistical methods for intractable models

### George Nicholson

Computational biostatistics, machine learning, precision medicine

### Chieh-Hsi (Jessie) Wu

Computational statistics, machine learning, stratified medicine.

## Graduate Students

### Moustafa Abdalla

Multi-view Learning, Time-series modelling, Statistical Genetics, Drug Development, High-throughput screening

### Fadhel Ayed

Bayesian nonparametrics, Machine Learning

### Christian Carmona Perez

Methods for Bayesian modeling under mispecification

### Anthony Caterini

High-Dimensional 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 Gram-Hansen

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

### Zhiyuan Hu

Single-Cell Analysis, Ovarian Cancer, Genomics

### 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

### Cian Naik

Bayesian nonparametrics, Statistical Network Analysis

### Francesca Panero

Bayesian random graphs, Bayesian nonparametrics, disclosure risk

### Emilia Pompe

MCMC methods, Bayesian statistics

### Dominic Richards

Optimisation, Monte Carlo Methods

### David Rindt

Nonparametric measures of dependence

### Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

### Sebastian Schmon

Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, High-Dimensional Statistics

### Jean-Francois Ton

Kernel methods, Meta-learning

### Joost van Amersfoort

Machine Learning, Variational Inference, Uncertainty Estimation

### Matthew Willetts

Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification

### Jin Xu

Meta-learning, deep generative models, representation learning, probabilistic inference

### Yuan Zhou

Probabilistic programming, Machine learning, Bayesian inference

## Former Members

- Louis Aslett
- Remi Bardenet
- Marco Battiston
- Benjamin Bloem-Reddy
- Levi Boyles
- Ryan Christ
- Lloyd Elliott
- Seth Flaxman
- Leonard Hasenclever
- Pierre Jacob
- Hyunjik Kim
- Ho Chung Leon Law
- Juho Lee
- Thibaut Lienart
- Xiaoyu Lu
- Simon Lyddon
- Jovana Mitrovic
- Konstantina Palla
- Valerio Perrone
- Jennifer Rogers
- Andrew Roth
- Patrick Rubin-Delanchy
- Tammo Rukat
- Stefan Webb
- Qinyi Zhang