Organisers:
David Gunawan (University of Wollongong)
Ajay Jasra (The Chinese University of Hong Kong)
Leah South (Queensland University of Technology)
Matthew T. Moores (University of Wollongong)
Sumeetpal S. Singh (University of Wollongong)
Susan Wei (Monash University)

Program Description:
One of the outstanding successes in Machine Learning over the last five years is the advancement in modelling of complex physical processes. Perhaps the best-known examples of this success are computer-generated human conversations, or photo-realistic images. Another important recent advancement is in modelling operators between function spaces. A good operator model can greatly expedite the execution of models based on partial-differential equations, which could have profound implications for their numerous real-world applications, for example, more up-to-date weather forecasting. At the heart of all these disparate physical process models are neural network architectures, which have many thousands of parameters to be learnt. In fact, some neural network modelling efficacy studies assume they have infinitely many parameters, as larger networks have better modelling flexibility. Bayesian learning of model parameters, a popular methodology in Statistics, is the gold standard as it finds the best parameter values with full quantification of their uncertainty. Unfortunately, Bayesian learning is presently infeasible for these modern physical process models, as the learning problem is extremely high-dimensional, way beyond applications in Statistics where Bayesian computational methods have been so successful. As such, non-Bayesian approaches have proliferated in Machine Learning. This program will convene experts in Machine Learning generative modelling, and from the Applied Mathematics and Statistics communities, experts in high-dimensional Bayesian methodology, to find novel Bayesian solutions to learning these complex physical process models. A major potential outcome of this program is improved uncertainty quantification for both the forecasts and the outcomes of interventions derived from these models. This will reduce the potential for adverse real-life consequences.

Program Structure:

Click here for talk titles and abstracts

Participant List:
Axel Finke (Newcastle University)
Yingzhen Li (Imperial College London)
Josh Bon (Université Paris-Dauphine)
Xin Tong (National University of Singapore)
David Gunawan (University of Wollongong)
Adrien Corenflos (University of Warwick)
Kengo Kamatani (Institute of Statistical Mathematics)
Maurizio Filippone (King Abdullah University of Science and Technology)
Patricia Ning (Texas A&M University, College Station)
Maria De Iorio (National University of Singapore)
Dootika Vats (Indian Institute of Technology Kanpur)
Jeremy Heng (ESSEC Business School)
Marina Riabiz (King’s College London)
Daniel Paulin (College of Computer Science and Division of Mathematics, Nanyang Technological University)
Samuel Power (School of Mathematics, University of Bristol)
Clara Grazian (University of Sydney)
Sahani Pathiraja (UNSW)
Sumeetpal Singh (Mathematics and Applied Statistics)
Matti Vihola (University of Jyväskylä)
Susan Wei (Monash University)
Joona Karjalainen (University of Jyväskylä)
Pierre Del Moral (INRIA)
Florian Maire (Université de Montréal)
Shuigen Liu (National University of Singapore)
Matthew Moores (Lappeenranta-Lahti University of Technology (LUT))

Registration:

• Registration is now closed
• Arrival date: 22 June 2025
• Departure date: 4 July 2025

ASSOCIATED EVENTS
MATRIX Wine and Cheese Afternoon 24 June 2025
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.