James Foster

Research interests

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.

Alongside my interest in SDEs, I have worked on machine learning projects in collaboration with members of the DataSıg team. Here we introduced new differential equation models and algorithms, inspired by rough path theory, for tackling problems involving multivariate time series.