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

Juho Lee

Juho Lee

Bayesian nonparametric models, random graphs

George Nicholson

George Nicholson

Computational biostatistics, machine learning, precision medicine

Tom Rainforth

Tom Rainforth

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

Andrew Roth

Andrew Roth

Computational statistics, Machine learning, Genomics, Cancer evolution

Graduate Students

Moustafa Abdalla

Moustafa Abdalla

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

Fadhel Ayed

Fadhel Ayed

Bayesian nonparametrics, Machine Learning

Anthony Caterini

Anthony Caterini

High-Dimensional Statistics, Monte Carlo Methods, Variational Inference

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, optimal experiment design, deep learning, probabilistic programming

Bradley Gram-Hansen

Bradley Gram-Hansen

Probabilistic Programming, Monte Carlo Methods, Variational Inference, Computational Sustainability, Quantum Information

Frauke Harms

Frauke Harms

Bayesian nonparametrics, machine learning, stochastic geometry

Zhiyuan Hu

Zhiyuan Hu

Single-Cell Analysis, Ovarian Cancer, Genomics

Hyunjik Kim

Hyunjik Kim

Gaussian Processes, probabilistic inference, deep generative models

Charline Le Lan

Charline Le Lan

Probabilistic Inference, Deep Learning, Reinforcement Learning

Xiaoyu Lu

Xiaoyu Lu

Machine learning, Gaussian Process, Bayesian Optimization, Adaptive Importance Sampling

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

Statistical machine learning, probabilistic inference, Gaussian Processes, multi-view learning

Emile Mathieu

Emile Mathieu

Probabilistic inference, Deep learning, Generative models

Xenia Miscouridou

Xenia Miscouridou

Statistical Machine Learning, Deep Generative Models, Bayesian Random Graphs

Tim G. J. Rudner

Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

Sebastian Schmon

Sebastian Schmon

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

Stefan Webb

Stefan Webb

deep learning, deep generative models, probabilistic inference, neural network verification

Matthew Willetts

Matthew Willetts

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

Jin Xu

Jin Xu

Probabilistic inference, deep generative models, representation learning

Qinyi Zhang

Qinyi Zhang

Statistical Machine Learning, Kernel Method, Nonparametric Association Measures

Alumni