Staff
James Foster

Department of Mathematical Sciences

Dr James Foster

Research interests

  • Numerical methods for stochastic differential equations (SDEs)
  • Langevin Monte Carlo and applications of SDEs to machine learning
  • Neural differential equations and kernel methods for time series data

James is interested in stochastic numerics, differential equations and their application to machine learning. His mathematics research has mainly focused on developing numerical approximations for stochastic differential equations (SDEs), which have subsequently been applied to prominent SDEs within data science, such as Langevin dynamics and Neural SDEs. Alongside his SDE research, James has worked on differential equation models and algorithms in machine learning, especially focusing on problems involving time series. These projects build upon ideas from an area known as “rough path theory” and have been in collaboration with several members of the DataSıg team (https://datasig.web.ox.ac.uk/).

 

LINKS:

James Foster on the University of Bath Research Portal

Lead Supervisors

Eric Hester

  • Asymptotic analysis of PDEs
  • Spectral algorithms for numerical PDE solvers
  • Multiphase fluid mechanics
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Stephen Wilson

Department of Mathematical Sciences

  • Fluid mechanics, especially thin-film flows, rivulets and evaporating droplets.
  • Non-Newtonian fluid mechanics, especially liquid crystals and thixotropic fluids.
  • More generally, the use of a range of mathematical (namely asymptotic, analytical and numerical) methods to bring new insights into a wide range of “real world” problems.
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Michael Murray

Department of Mathematical Sciences

  • Optimization: implicit regularization, geometry of the loss landscape
  • Generalization: benign and tempered overfitting, phase transitions in performance with respect to compute and data
  • Understanding emerging paradigms in ML, e.g., in-context learning, transformers etc.
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Federico Cornalba

Department of Mathematical Sciences

  • Modelling of large-scale interacting particle systems
  • Analysis and numerics of stochastic PDEs of Fluctuating Hydrodynamics
  • Reinforcement Learning methods
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Avi Mayorcas

Department of Mathematical Sciences

  • Regularisation by noise in stochastic partial and ordinary differential equations
  • Stochastic quantisation of physical field theories
  • Game theory and mean field dynamics in macroeconomics and finance
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Christoforos Panagiotis

Department of Mathematical Sciences

  • Percolation and lattice spin models
  • Probability on groups
  • Self-avoiding walk
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Matthew Schrecker

Department of Mathematical Sciences

  • Analysis of Partial Differential Equations
  • Fluid dynamics (especially free boundary problems and nonlinear singularity formation)
  • Shock waves (their formation, structure and dynamics)
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Haiyan Zheng

Department of Mathematical Sciences

  • Adaptive designs in clinical trials
  • Bayesian data augmentation
  • Finite mixture distributions
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Andreas Kyprianou

Andreas was instrumental in the development of SAMBa and was Co-Director from its inception until the end of 2022. He has left the University of Bath to take up the role of Chair of Probability at the University of Warwick.

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Jennifer Tweedy

Department of Mathematical Sciences

  • Mathematical medicine
  • Fluid mechanics
  • Mathematical modelling
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