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

François Caron

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

Robin Evans

Robin Evans

Graphical models, causality, algebraic statistics

Chris Holmes

Chris Holmes

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

Geoff Nicholls

Geoff Nicholls

Statistical modeling, Bayes Methods, Monte Carlo Methods.

Jennifer Rogers

Jennifer Rogers

Statistical methodology in medical research, predominantly clinical trial research

Judith Rousseau

Judith Rousseau

Bayesian statistics, Asymptotics, Nonparametric statistics

Dino Sejdinovic

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

Yee Whye Teh

Yee Whye Teh

Bayesian nonparametrics, probabilistic learning, deep learning

Affiliated Faculty

Sarah Filippi

Sarah Filippi

Statistical machine learning and Bayesian statistics motivated by applications in biomedicine

Post-docs

M. Azim Ansari

M. Azim Ansari

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

Yunlong Jiao

Yunlong Jiao

Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics

Luke Kelly

Luke Kelly

Statistical methods for intractable models

George Nicholson

George Nicholson

Computational biostatistics, machine learning, precision medicine

Andrew Roth

Andrew Roth

Computational statistics, Machine learning, Genomics, Cancer evolution

Graduate Students

Anthony Caterini

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Applied Mathematics

Ryan Christ

Ryan Christ

Genomics, Computational statistics, Network Analysis

Sam Davenport

Sam Davenport

Gaussian Processes, fMRI data, Resampling methods, Random Field Theory

Adam Foster

Adam Foster

Probabilistic inference, probabilistic programming, Bayesian nonparametrics

Frauke Harms

Frauke Harms

Bayesian nonparametrics, machine learning, stochastic geometry

Hyunjik Kim

Hyunjik Kim

Gaussian Processes, probabilistic inference, deep generative models

Thibaut Lienart

Thibaut Lienart

Inference on graphical models, expectation propagation, particle methods

Xiaoyu Lu

Xiaoyu Lu

Machine learning, reinforcement learning, stochastic processes

Simon Lyddon

Simon Lyddon

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

Chris J. Maddison

Chris J. Maddison

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

Kaspar Märtens

Kaspar Märtens

Computational statistics, Bayesian machine learning, multi-view learning

Emile Mathieu

Emile Mathieu

Bayesian nonparametrics, probabilistic inference, deep learning

Tammo Rukat

Tammo Rukat

Matrix Factorisation, Bayesian Neural Nets, Deep Generative Models, Statistical Genetics

Sebastian Schmon

Sebastian Schmon

Bayesian Statistics, Computational Statistics, Markov chain Monte Carlo

Stefan Webb

Stefan Webb

deep learning, deep generative models, probabilistic inference

Matthew Willetts

Matthew Willetts

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

Qinyi Zhang

Qinyi Zhang

Statistical Machine Learning, Kernel Method, Nonparametric Association Measures

Alumni