Jacinta Holloway-Brown

Dr Jacinta Holloway-Brown


School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Dr Jacinta Holloway-Brown is a lecturer in the School of Computer and Mathematical Sciences at University of Adelaide, South Australia. Previously, Jacinta worked as a postdoctoral fellow in the Queensland University of Technology (QUT) Centre for Data Science, and a research associate in the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) at QUT, where she also completed a PhD.

She has developed and taught workshops on statistical machine learning methods for analysing satellite imagery data for the United Nations, and run these workshops in Bogota, Colombia and Bangkok, Thailand. She also worked for the Australian Bureau of Statistics for years, more recently in methodology and macroeconomic statistics roles.

She has degrees in statistics, journalism and economics, and enjoys working collaboratively to use data science to better understand, monitor and manage the environment.

My research is about working with imperfect data and models to better monitor the environment. 

To do this I focus on developing hybrid approaches of statistical methodology and machine learning, working across mathematical and biological disciplines on environmental problems. I enjoy working collaboratively with experts, including ecologists and remote sensing scientists, to ensure the methodological tools I create are genuinely useful and impactful both in and beyond the statistical domain. 

My methodology research interests include spatial, Bayesian and adaptive sampling design methods, with a focus on making these scalable and meaningful for big data and remote sensing analysis. I also work on enhancing machine learning methods by combining these with traditional statistical approaches to account for spatial information and quantify uncertainty of predictions. This is important in many applications, and particularly environmental monitoring where systems can fluctuate and data are often missing, disparate or limited.

My environmental interests include, but are not limited to, forest cover monitoring, modelling biodiversity, weather data and environmental change of our terrestrial and marine environments over time. 

  • STATS 1005 Statistical Analysis and Modelling I (2022-2023)
  • MATHS 1005 - Critical Evaluation in Data Science (2023


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