Dr Helen Mayfield

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Postdoctoral Researcher, School of Built Environment

PhD (University of Queensland), Bachelor of information technology with Honours class I, Bachelor of Arts -Psychology and Spanish (University of Queensland)

Helen’s research interests are in both biodiversity conservation and human health, and how these decisions in the fields could be optimised to achieve mutual gains for people and the environment.  Her underlying goal is to facilitate evidence-based decisions in these fields by making data modelling and decision science more accessible to decision makers, regardless of the quality or nature of their data. To this effect, she works with a range of data types from high quality sero-prevalence data collected from field studies, to expert judgements obtained via structured expert elicitation.

In 2015 Helen completed her PhD in the application of machine learning to predicting deforestation, under the supervision of Professor Marc Hockings. She has since worked with several universities on various evidenced-based decision-making scenarios including exploring the drivers of infectious diseases in the Pacific and how these can be used to customise prevention strategies, and optimising sampling methods for locating infection hotspots (Australian National University).  Helen has also worked closely with the NSW Department of Industry, Planning and Environment to create guidelines and facilitate workshops on the use of conceptual modelling and structured expert elicitation to estimate a species response to management using expert judgement, for both generating targets for adaptive management and estimating the gain from management in relation to biodiversity offsetting (University of Queensland).  Her current work with QUT uses expert-led Bayesian networks to evaluate the effects of different policy instruments on the adoption of environmental beneficial farming practices in the Great Barrier Reef Catchment area. She is a member of the Griffith University systems modelling group, and the Australian Bayesian Network Society.