I am interested in stochastic numerics, differential equations and their applications to machine learning.
Since my time as a graduate student, I have particularly enjoyed the numerical analysis of Brownian motion and Stochastic Differential Equations (SDEs). This research has focused on developing numerical methods and applying them to prominent SDEs in data science, such as Langevin dynamics and Neural SDEs. You can find some of my numerical methods in the Diffrax library (full credit goes to Andraž Jelinčič and Patrick Kidger for making this possible).
Alongside my interest in SDEs, I have worked on machine learning projects in collaboration with members of the DataSig team, where we introduced new differential equation models and algorithms, inspired by rough path theory, for multivariate time series problems.
Sam McCallum, and James Foster
Preprint
[Slides]
James Foster
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James Foster and Andraž Jelinčič
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Andraž Jelinčič, James Foster and Patrick Kidger
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Andraž Jelinčič, Jiajie Tao, William F. Turner, Thomas Cass, James Foster and Hao Ni
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James Foster, Gonçalo dos Reis and Calum Strange
SIAM Journal on Numerical Analysis (2024)
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James Foster and Karen Habermann
Combinatorics, Probability and Computing (2023)
James Foster, Terry Lyons and Vlad Margarint
Journal of Statistical Physics (2022)
James Foster, Terry Lyons and Harald Oberhauser
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[Slides]
Patrick Kidger, James Foster, Xuechen Li and Terry Lyons
Neural Information Processing Systems (2021)
Cristopher Salvi, Thomas Cass, James Foster, Terry Lyons and Weixin Yang
SIAM Journal on Mathematics of Data Science (2021)
Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser and Terry Lyons
International Conference on Machine Learning (2021)
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James Morrill, Cristopher Salvi, Patrick Kidger, James Foster and Terry Lyons
International Conference on Machine Learning (2021)
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Patrick Kidger, James Morrill, James Foster and Terry Lyons
Neural Information Processing Systems, Spotlight (2020)
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James Foster, Terry Lyons and Harald Oberhauser
SIAM Journal on Numerical Analysis (2020)
[Slides] [Poster]