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

### Emile Mathieu

Probabilistic inference, Deep learning, Generative models, Representation Learning, Geometry

### George Nicholson

Computational biostatistics, machine learning, precision medicine

## Graduate Students

### Moustafa Abdalla

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

### Freddie Bickford Smith

Deep Learning, Uncertainty Estimation

### Shahine Bouabid

Kernel Methods, Gaussian processes, Climate emulation

### 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

### 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

### Zhiyuan Hu

Single-Cell Analysis, Ovarian Cancer, Genomics

### Robert Hu

Machine Learning, Kernel Methods, Causal Inference

### 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, Meta-Learning, Bayesian Optimisation

### Ning Miao

Deep generative models

### Cian Naik

Bayesian nonparametrics, Statistical Network Analysis

### 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

### Jean-Francois Ton

Kernel methods, Meta-learning

### Hanwen Xing

Computational methods, Bayesian inference

### Jin Xu

Meta-learning, equivariance in deep learning

### Schyan Zafar

Monte Carlo methods, Multivariate stochastic processes

### Sheheryar Zaidi

Statistical Machine Learning, Deep Learning

## Former Members

- Louis Aslett
- Fadhel Ayed
- Remi Bardenet
- Marco Battiston
- Benjamin Bloem-Reddy
- Levi Boyles
- Ryan Christ
- Giuseppe Di Benedetto
- Lloyd Elliott
- Seth Flaxman
- Bradley Gram-Hansen
- Leonard Hasenclever
- Pierre Jacob
- 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
- David Rindt
- Jennifer Rogers
- Andrew Roth
- Patrick Rubin-Delanchy
- Tammo Rukat
- Sebastian Schmon
- Joost van Amersfoort
- Stefan Webb
- Matthew Willetts
- Chieh-Hsi (Jessie) Wu
- Qinyi Zhang
- Yuan Zhou