Rough path theory is a relatively new field of mathematics, introduced in the late 90s. Inherently a multidisciplinary theory in which one will see semi-Riemannian geometry, Lie groups and algebras, analysis, and shuffle algebras, it may seem surprising that the theory has had huge success in the last two decades to treat stochastic ordinary and partial differential equations. More recently, tools from rough path theory have even been utilised to deal with problems in machine learning.

This conference focuses on rough path theory and its applications to applied fields such as stochastic differential equations and machine learning. Our goals are twofold:

  1. Bring together students and researchers across partial differential equations, dynamical systems, stochastic differential equations and machine learning to demonstrate how rough path techniques can be utilised to solve applied problems, and
  2. To foster discussion and possible collaboration between attendees.

Invited Speakers:

  • Jasper Barr (Australian National University) is a PhD student interested in how rough paths and other modern techniques can be utilised to study regime-switching stochastic differential equations
  • Xi Geng (University of Melbourne) is an expert in rough path theory and rough differential equations driven by Gaussian processes.
  • Liam Hodgkinson (University of Melbourne) is an expert in probabilistic machine learning, utilising analyticial probability theory to understand and develop new methodology.
  • David Lee (Sorbonne Université) is an early career researcher focused on the interactions of partial differential equations and probability theory.
  • Esmée Theewis (TU Delft) is a PhD Student studying large deviations of stochastic evolution equations in Banach spaces

Registration Expression of Interest: Google form

Jasper Barr – Australian National University
Sheng Wang – University of Melbourne


This symposium is supported by MATRIX and AMSI through the MATRIX-AMSI PhD Student Research Collaboration Scheme:

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