Jacinta is a research associate and PhD candidate at the Australian Research Council centre of excellence for mathematical and statistical frontiers (ACEMS) at Queensland University of Technology in Brisbane, Australia. Her current research focuses on machine learning and spatial analysis of remote sensing data for environmental statistics. She 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.
Jacinta has degrees in statistics, journalism and economics. She has also previously worked as a senior media officer for the Queensland Health Payroll Commission of Inquiry, and the Queensland Department of Justice and Attorney-General.
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. British Ecological Society "Quantitative Ecology Virtual Conference".
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.