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

## Faculty

### François Caron

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

### 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.

### Jennifer Rogers

Statistical methodology in medical research, predominantly clinical trial research

### 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

### Louis Aslett

Encrypted statistical methods, parallel MCMC methods, high performance computing, reliability theory

### Marco Battiston

Bayesian nonparametrics

### Seth Flaxman

Scalable spatiotemporal statistics and Bayesian machine learning for public policy and social science

### Luke Kelly

Statistical methods for intractable models

### Konstantina Palla

non-parametric Bayesian methods and models

### Patrick Rubin-Delanchy

Data Science, machine-learning, networks, anomaly detection, cyber-security

### Chieh-Hsi (Jessie) Wu

Computational statistics, machine learning, stratified medicine.

## Graduate Students

### Giuseppe Di Benedetto

Bayesian nonparametrics, Machine Learning

### Frauke Harms

Bayesian nonparametrics, machine learning, stochastic geometry

### Leonard Hasenclever

Large scale machine learning, probabilistic inference, deep learning

### Hyunjik Kim

Gaussian Processes, probabilistic inference, deep generative models

### Ho Chung Leon Law

Kernel methods, machine learning

### Thibaut Lienart

Inference on graphical models, expectation propagation, particle methods

### Xiaoyu Lu

Machine learning, reinforcement learning, stochastic processes

### Simon Lyddon

Bayesian statistics, decision theory, computational statistics, machine learning.

### Chris J. Maddison

Probabilistic inference, Monte Carlo methods, neural networks, point processes

### Kaspar Märtens

Computational statistics, Bayesian machine learning, multi-view learning

### Xenia Miscouridou

Bayesian nonparametrics, Machine Learning, Networks

### Jovana Mitrovic

Kernel methods, deep learning

### Valerio Perrone

Bayesian nonparametrics, deep learning

### Tammo Rukat

bayesian neural nets, deep generative models, statistical genetics

### Sebastian Schmon

Bayesian Statistics, Computational Statistics, Markov chain Monte Carlo

### Stefan Webb

deep learning, deep generative models, probabilistic inference

### Matthew Willetts

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

### Qinyi Zhang

Statistical Machine Learning, Kernel Method, Nonparametric Association Measures