The Australian Research Council released its Discovery Early Career Researcher Awards for 2020, and three ACEMS researchers are on that list. They are Dr. Kate Helmstedt from ACEMS at QUT, Dr David Frazier from ACEMS at Monash University, and Dr Susan Wei from ACEMS at The University of Melbourne. Here is a look at their projects:
Mathematically optimal R&D for coral reef conservation
Dr David Frazier (Monash University): $376,496
Consequences of Model Misspecification in Approximate Bayesian Computation
In almost any empirical application, the model the analyst is working with constitutes a misspecified description of the true process that has generated the data. While the method of Approximate Bayesian computation (ABC) is now a staple in the toolkit of the applied modeller, the impact of misspecification in ABC is unknown. This project aims to undertake a rigorous study into the behaviour of ABC under model misspecification. Expected outcomes include new theoretical results for ABC under misspecification and new methods capable of detecting/mitigating model misspecification. This project will provide significant benefits in all spheres where reliable, robust statistical inference methods are required in order to make reliable decisions.
Dr Susan Wei (The University of Melbourne): $349,586
Making Machine Learning Fair(er)
This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of this project include improved techniques for imposing invariance on deep learning algorithms. This should provide significant benefits to the general public by contributing to the advancement of socially responsible and conscientious machine learning.
For a complete look at the ARC Discovery Early Career Researcher Awards, head to the ARC website page.