##### Alexander Cox

######
stochastic control
monte carlo methods
optimal transport

##### Beate Ehrhardt

######
hypothesis testing
bayesian statistics
machine learning
networks
optimal experimental design
causality

##### Chris Budd

######
industrial mathematics
dynamical systems
public engagement
numerical analysis
deep learning

##### Christian Rohrbeck

######
bayesian computing
environmental modelling
extreme value analysis
spatial statistics

##### Clarice Poon

######
compressed sensing
structured regularisation
super resolution
optimisation

##### Eike Mueller

######
scientific computing
massively parallel solvers for pdes
(multilevel) monte carlo methods
multigrid algorithms

##### Emiko Dupont

######
spatial statistics
wavelets
machine learning
environmental applications

##### James Foster

######
stochastic differential equations
machine learning
rough analysis

##### Jie Zhang

######
algorithmic game theory
digital economy
blockchain protocols

##### Jon Dawes

######
dynamical systems
reservoir computing

##### Kari Heine

######
sequential monte carlo
parallelism
high-dimensional problems
mcmc
population genetics

##### Karim Anaya-Izquierdo

######
statistical engineering
geometrical mcmc
distribution theory using geometry
statistical epidemiology

##### Lisa Kreusser

######
dynamical systems
differential equations
numerical analysis
deep learning

##### Luca Zanetti

######
algorithms for network analysis
clustering
markov chains
spectral graph theory

##### Matt Nunes

######
bayesian computation
dimension reduction
image processing
networks
time series
wavelets

##### Mohammad Golbabaee

######
signal and image processing
low-complexity models
compressed sensing
computational medical imaging
large-scale machine learning

##### Neill Campbell

######
visual computing
unsupervised learning
bayesian non-parametrics
uncertainty quantification

##### Olga Isupova

######
bayesian methods
topic models
unsupervised learning
ml for conversation

##### Sandipan Roy

######
high-dimensional inference
graphical models
machine learning
non-parametric regression
subsampling
parallel optimization

##### Sergey Dolgov

######
linear and multilinear algebra
tensor-product decompositions

##### Silvia Gazzola

######
regularization of inverse problems
imaging problems
numerical linear algebra

##### Tatiana Bubba

######
tomographic inverse problems
sparse regularisation
optimisation
deep learning in imaging

##### Teo Deveney

######
deep learning
bayesian inference
differential equations

##### Theresa Smith

######
spatial statistics
bayesian computing
health applications

##### Tom Fincham Haines

######
bayesian non-parametrics
graphical models
active learning
directional statistics
density estimation

##### Tony Shardlow

######
stochastic differential equations
numerical analysis
bayesian inverse problems

##### Vinay Namboodiri

######
multi-modal learning
visual recognition
sparse supervision
probabilistic adversarial techniques
explainable ai

##### Xi Chen

######
bayesian inference & reasoning
machine learning
statistical signal processing
monte carlo methods
probabilistic sampling techniques

##### Yury Korolev

######
inverse problems and imaging
machine learning in infinite dimensions
non-smooth variational problems

##### Özgür Şimşek

######
reinforcement learning
regularisation
learning from small samples