Presented by Professor Sally Cripps

Date: 12pm-1pm, Wednesday 29th Nov 2023
Venue: F23 Michael Spence Building Auditorium (2) 105

Understanding and identifying which factors impact outcomes is at the core of scientific discovery. Graphical models and the Bayesian paradigm are useful frameworks to describe and analyse these cause-and-effect relationships however, they are not without challenges and limitations.  This talk demonstrates the potential and pitfalls of such methods with reference to applications in the physical and social sciences.

About the speaker

Sally Cripps is an internationally recognized scholar and leader in Bayesian Machine Learning (ML) and Artificial Intelligence (AI). In addition to her role as Director of Technology at the Human Technology Institute, she is a Professor of Mathematics and Statistics at the UTS. Sally was also cofounder and co-director of the University of Sydney’s Centre for Translation Data Science (CTDS), founder and Director of the Australian Research Council’s Industrial Transformation Training Centre (ARC ITTC) Data Analytics for Resources and Environments (DARE). Most recently Sally was Research Director of Analytics and Decision Science and Science Director of the Next Gen AI Training Programme in CSIRO’s Data61. Sally’s research focuses on the development of new foundational methods in AI to address global challenges. In recognition of the quality of her research, Sally was awarded an ARC Future Fellowship and a visiting scholar fellowship to the Alan Turing Institute in the UK.