Faculty

Beate Ehrhardt
Beate Ehrhardt
hypothesis testing bayesian statistics machine learning networks optimal experimental design causality
Clarice Poon
Clarice Poon
compressed sensing structured regularisation super resolution optimisation
Eike Mueller
Eike Mueller
scientific computing massively parallel solvers for pdes (multilevel) monte carlo methods multigrid algorithms
Evangelos Evangelou
Evangelos Evangelou
linear models geostatistics time series
Jonathan Bartlett
Jonathan Bartlett
missing data measurement error causal inference
Kari Heine
Kari Heine
sequential monte carlo parallelism high-dimensional problems mcmc population genetics
Karim Anaya-Izquierdo
Karim Anaya-Izquierdo
statistical engineering geometrical mcmc distribution theory using geometry statistical epidemiology
Luca Zanetti
Luca Zanetti
algorithms for network analysis clustering markov chains spectral graph theory
Matt Nunes
Matt Nunes
bayesian computation dimension reduction image processing networks time series wavelets
Matthias Ehrhardt
Matthias Ehrhardt
imaging machine learning optimisation
Mohammad Golbabaee
Mohammad Golbabaee
signal and image processing low-complexity models compressed sensing computational medical imaging large-scale machine learning
Neill Campbell
Neill Campbell
visual computing unsupervised learning bayesian non-parametrics uncertainty quantification
Sandipan Roy
Sandipan Roy
high-dimensional inference graphical models machine learning non-parametric regression subsampling parallel optimization
Sergey Dolgov
Sergey Dolgov
linear and multilinear algebra tensor-product decompositions
Silvia Gazzola
Silvia Gazzola
regularization of inverse problems imaging problems numerical linear algebra
Theresa Smith
Theresa Smith
spatial statistics bayesian computing health applications
Tom Fincham Haines
Tom Fincham Haines
bayesian non-parametrics graphical models active learning directional statistics density estimation
Tony Shardlow
Tony Shardlow
stochastic differential equations numerical analysis bayesian inverse problems
Özgür Şimşek
Özgür Şimşek
reinforcement learning regularisation learning from small samples

Researchers

Adam Hartshorne

Working with Neill Campbell

Adwaye Rambojun

Working with Neill Campbell and Tony Shardlow

Alessandro Di Martino

Working with Neill Campbell

Allen Hart

Working with James Hook

Amelie Klein

Working with Kari Heine

Andreas Theophilou

Working with Özgür Şimşek

Andrew Lawrence

Working with Neill Campbell

Arron Gosnell

Working with Evangelos Evangelou

Connr Gascoigne

Working with Theresa Smith

Dan Burrows

Working with Kari Heine

Dan Green

Working with Melina Freitag

David Fernandes

Working with Neill Campbell

Ed Wong

Working with Tom Fincham Haines

Eleanor Barry

Working with Jonathan Bartlett

Elizabeth Gray

Working with Evangelos Evangelou

Emelie Barman

Working with Neill Campbell

Eric Castillo

Working with Melina Freitag

Fangpei Wang

Working with Clarice Poon and Tony Shardlow

Federico Cornalba

Working with Tony Shardlow and J. Zimmer

James Evans

Working with Karim Anaya-Izquierdo

Jan Malte Lichtenberg

Working with Özgür Şimşek

Jenny Delos Reyes

Working with Tony Shardlow and J. White

Joshua Evans

Working with Özgür Şimşek

Lizhi Zhang

Working with Tiago de Paula Peixoto

Malena Sabate Landman

Working with Silvia Gazzola

Matthew Hosier

Working with Tom Fincham Haines

Michele Firmo

Working with Tony Shardlow

Nadeen Khaleel

Working with Theresa Smith

Paul Russell

Working with Özgür Şimşek

Paul Secular

Working with Sergey Dolgov

Sebastian Morel-Balbi

Working with Tiago de Paula Peixoto

Sebastian Stolze

Working with Evangelos Evangelou

Teo Deveney

Working with Tony Shardlow

Tom Pennington

Working with R. Scheichl and K. Anaya-Izquierdo