# ARC Awards DECRA's to ACEMS Researchers

The Australian Research Council released its Discovery Early Career Researcher Awards for 2020, and two ACEMS researchers are on that list. They are Dr. Kate Helmstedt from ACEMS at QUT, and Dr David Frazier from ACEMS at Monash University. Here is a look at their projects:

### ## k-helmstedt.jpg Dr Kate Helmstedt Dr Kate Helmstedt (QUT): $427,082 Mathematically optimal R&D for coral reef conservation This project aims to develop mathematical methodologies for optimising Research & Development (R&D) of technologies that will secure complex and uncertain ecosystems into the future. Current conventional management approaches will not prevent the degradation of threatened ## d-frazier.jpg Dr David Frazier ecosystems like the Great Barrier Reef, so new technologies are needed. The biggest challenge in choosing these technologies is the long delay between development and deployment, in which time ecosystem function may collapse and complext dynamic ecological and social systems will change. The mathematical methods and theory developed will inform a Great Barrier Reef case study, and will be ready for rapid application to other ecosystems as the urgent need arises. ### 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.