Dr Jacinta Holloway Brown is a Postdoctoral Fellow in the QUT School of Mathematical Sciences and Centre for Environment. She develops new statistical machine learning methods to solve environmental and sustainable development problems. Her research currently focuses on developing new Bayesian experimental design approaches for modelling biodiversity and using remote sensing analysis to monitor change in forests.
In her previously role as a research associate in ACEMS, Jacinta has developed and taught hands on workshops on machine learning methods for analysing satellite imagery data for the United Nations, and run these workshops in Bogota, Colombia and Bangkok, Thailand. Previously, she worked for the Australian Bureau of Statistics for years, more recently in methodology and tourism statistics roles.
Machine Learning methods for Big Data
Satellite image analysis
Sustainable Development Goals
Grad Dip(Applied Statistics) (Swinburne Institute of Technology)
Bachelor of Business (Economics)/Journalism (Queensland University of Technology)
That’s the aim of new research just published by ACEMS’ researchers in the Journal of Big Data. Led by Jacinta Holloway Brown from ACEMS at QUT, the researchers developed a new statistical method to predict forest cover in satellite images where portions of the image are blocked by cloud cover. Not only that, the new method also calculates a probability to show how confident the prediction is.
Invited talks, refereed proceedings and other conference outputs
Adams, M. P., Sisson S., O'Brien K. R., Helmstedt K. J., Baker C. M., Koh E. J. Y., et al.
(2020). Propagating uncertainty through to model forecasts: deterministic Lotka-Volterra systems as a case study. Quantitative Ecology Virtual Meeting 2020.
Holloway, J., Mengersen KL., & Helmstedt K. J.
(2018). Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals. IAOS-OECD Better Statistics for Better Lives.