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

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

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

Louis Aslett

Louis Aslett

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

Seth Flaxman

Seth Flaxman

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

Luke Kelly

Luke Kelly

Statistical methods for intractable models

Graduate Students

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

Tammo Rukat

Tammo Rukat

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, Gaussian processes, Time series segmentation and classification

Qinyi Zhang

Qinyi Zhang

Statistical Machine Learning, Kernel Method, Nonparametric Association Measures

Alumni