Alexander Cox
Alexander Cox
stochastic control monte carlo methods optimal transport
Beate Ehrhardt
Beate Ehrhardt
hypothesis testing bayesian statistics machine learning networks optimal experimental design causality
Cangxiong Chen
Cangxiong Chen
machine learning differential privacy
Chris Budd
Chris Budd
industrial mathematics dynamical systems public engagement numerical analysis deep learning
Christian Rohrbeck
Christian Rohrbeck
bayesian computing environmental modelling extreme value analysis spatial statistics
Eike Mueller
Eike Mueller
scientific computing massively parallel solvers for pdes (multilevel) monte carlo methods multigrid algorithms
Emiko Dupont
Emiko Dupont
spatial statistics wavelets machine learning environmental applications
Evangelos Evangelou
Evangelos Evangelou
linear models geostatistics time series
Federico Cornalba
Federico Cornalba
stochastic pdes fluctuating hydrodynamics interacting particle systems machine learning
Georgios Exarchakis
Georgios Exarchakis
representation learning machine learning signal processing neural networks
James Foster
James Foster
stochastic differential equations machine learning rough analysis
Jie Zhang
Jie Zhang
algorithmic game theory digital economy blockchain protocols
Jon Dawes
Jon Dawes
dynamical systems reservoir computing
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
Lisa Kreusser
Lisa Kreusser
dynamical systems differential equations numerical analysis deep learning
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
Michael Murray
Michael Murray
machine learning deep learning
Neill Campbell
Neill Campbell
visual computing unsupervised learning bayesian non-parametrics uncertainty quantification
Oliver Feng
Oliver Feng
nonparametric statistics shape-constrained inference approximate message passing
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
Teo Deveney
Teo Deveney
deep learning bayesian inference differential equations
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
Vinay Namboodiri
Vinay Namboodiri
multi-modal learning visual recognition sparse supervision probabilistic adversarial techniques explainable ai
Wenbin Li
Wenbin Li
reinforcement learning continuous state approximation unsupervised learning
Xi Chen
Xi Chen
bayesian inference & reasoning machine learning statistical signal processing monte carlo methods probabilistic sampling techniques
Yury Korolev
Yury Korolev
inverse problems and imaging machine learning in infinite dimensions non-smooth variational problems
Özgür Şimşek
Özgür Şimşek
reinforcement learning regularisation learning from small samples