Organisers:
Jasper Barr (Australian National University)
Sheng Wang (University of Melbourne)

Description: 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.

Contact: jasper.barr@anu.edu.au

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 

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. 

Symposium webpage: https://sites.google.com/view/rpts23/home

Program Structure:

Monday: We will begin with a crash course in rough path theory for those who are unfamiliar.
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.

Registration:

• Expression of Interest  Google form deadline: 27 October 2023
• Registration for in-person participation is now closed.
• Arrival date: 26 November 2023
• Departure date: 1 December 2023

In-person participant list:
Jasper Barr (Australian National University )
Greg Baker (Australian National University )
Evan Markou (Australian National University)
Len Patrick Dominic Garces (University of Technology Sydney)
Tony Dooley (University of Texhnology Sydney)
Youjian Ouyang (University of Melbourne )
Sheng Wang (University of Melbourne )
Yanchao Yang (University of Melbourne)
Stanley Luk (University of Technology Sydney)
David Chan (University of Melbourne)
Jonathan Mavroforas (University of Technology, Sydney)
Kevin Qu (University of Sydney)
Thomas Scheckter (University of New South Wales)
Xi Geng (The University of Melbourne )
Nicola Di Vittorio (Macquarie University)
Vinayak Niraj (Indian Institute of Science)
Caroline (Yujia) Xiong (University of Melbourne)
   
Sponsors:
This symposium is supported by MATRIX and AMSI.

ASSOCIATED EVENTS

MATRIX Wine and Cheese Afternoon 28 November 2023
On the first Tuesday of each program, MATRIX provides a pre-dinner wine and cheese afternoon. Produce is locally-sourced to showcase delicacies from the region.