Adwaye Rambojun
Adwaye Rambojun
medical imaging bayesian statistics machine learning
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
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
James Foster
James Foster
stochastic differential equations machine learning rough analysis
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
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
Olga Isupova
Olga Isupova
bayesian methods topic models unsupervised learning ml for conversation
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
Tatiana Bubba
Tatiana Bubba
tomographic inverse problems sparse regularisation optimisation deep learning in imaging
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
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