Rough Path Theory Symposium 2023

About

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 a number of applied applied fields. 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.

The schedule will loosely be as follows: 

Monday: We will begin with a crash course in rough path theory.

Tuesday-Friday: The remainder of the conference will be comprised of morning talks from key speakers, short talks from participants in the afternoon, and poster sessions mixed with collaboration time. We may end earlier on Friday to allow for participant travel.

Registration

Registration for funded in-person participation is now closed. Those who wish to participate virtually are still encouraged to out the Google form. 

Note: No experience in rough paths is necessary! Anyone interested is encouraged to apply.

Participants will be invited to give a 20 minute talk discussing their research, and to bring a poster for discussion.

Organisers

Jasper Barr - Australian National University

Sheng Wang - University of Melbourne

Contact: jasper.barr@anu.edu.au

Speakers list

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 

Sheng Wang (University of Melbourne) is a PhD student with interests in rough path analysis and Malliavin calculus, especially in the radius of convergence of expected signature and logarithmic signature. 

Sponsors

This symposium is supported by MATRIX and AMSI.