Staff

  • Chris Budd

    Geometric integration, moving meshes, data assimilation

  • Federico Cornalba

    Stochastic PDEs, Fluctuating Hydrodynamics, Interacting Particle Systems, Machine Learning

  • Sergey Dolgov

    Numerical linear algebra, tensor decompositions, uncertainty quantification

  • Matthias Ehrhardt

    Imaging, machine learning, optimisation

  • James Foster

    stochastic differential equations, machine learning, rough analysis

  • Eric Hester

    Asymptotic analysis of PDEs, Spectral algorithms for numerical PDE solvers, Multiphase fluid mechanics

  • Yury Korolev

    Mathematical foundations of machine learning, Inverse problems and imaging, Calculus of variations

  • Lisa Kreusser

    Dynamical systems, deep learning, differential equations

  • Eike Mueller

    Numerical weather prediction, atmospheric modelling, scientific computing

  • Michael Murray

    Generalisation and optimisation in deep learning and machine learning.

  • Tristan Pryer

    High-performance computing, adaptivity

  • Tony Shardlow

    Stochastic differential equation, shape modelling, machine learning

  • Pranav Singh

    Quantum computing, optimal control, geometric integration, scientific machine learning

  • Euan Spence

    PDEs, high frequency scattering

  • Luca Zanetti

    Algorithms for networks, clustering, spectral graph theory, finite Markov chains

Emeritus and visiting staff

  • Tatiana Bubba

    Tomographic inverse problems, Sparse regularisation and optimisation, Deep learning in imaging

  • Silvia Gazzola

    Regularization of inverse problems, imaging problems, numerical linear algebra

  • Ivan Graham

    Numerical computation of waves and applications, domain decomposition, and uncertainty quantification

  • Adrian Hill

    Geometric integration

  • Clarice Poon

    Compressed sensing, structured regularisation, super resolution, optimisation

  • Alastair Spence

    Numerical Linear Algebra