Dr Jacinta Holloway-Brown
Lecturer
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.
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Journals
Year Citation 2023 Reading, L., Corbett, N., Holloway-Brown, J., & Bellis, L. (2023). Assessing the Relative Importance of Climatic and Hydrological Factors in Controlling Sap Flow Rates for a Riparian Mixed Stand. Agronomy, 13(1), 8.
2021 Holloway‐Brown, J., Helmstedt, K. J., & Mengersen, K. L. (2021). Interpolating missing land cover data using stochastic spatial random forests for improved change detection. Remote Sensing in Ecology and Conservation, 7(4), 649-665.
Scopus6 WoS62021 Holloway-Brown, J., Helmstedt, K. J., & Mengersen, K. L. (2021). Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land-cover data with multiple classes. International Journal of Remote Sensing, 42(10), 3756-3776.
Scopus7 WoS42020 Holloway-Brown, J., Helmstedt, K. J., & Mengersen, K. L. (2020). Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data. Journal of Big Data, 7(1), 23 pages.
Scopus3 WoS32020 Adams, M. P., Sisson, S. A., Helmstedt, K. J., Baker, C. M., Holden, M. H., Plein, M., . . . McDonald-Madden, E. (2020). Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data. Ecology Letters, 23(4), 607-619.
Scopus25 WoS19 Europe PMC62019 Leigh, C., Heron, G., Wilson, E., Gregory, T., Clifford, S., Holloway-Brown, J., . . . Peterson, E. E. (2019). Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species. PLoS ONE, 14(12), 17 pages.
Scopus10 WoS9 Europe PMC42019 Holloway, J., Helmstedt, K. J., Mengersen, K., & Schmidt, M. (2019). A decision tree approach for spatially interpolating missing land cover data and classifying satellite images. Remote Sensing, 11(15), 25 pages.
Scopus26 WoS202018 Holloway, J., & Mengersen, K. (2018). Statistical machine learning methods and remote sensing for sustainable development goals: A review. Remote Sensing, 10(9), 1365.
Scopus169 WoS130 -
Preprint
Year Citation 2023 Overstall, A. M., Holloway-Brown, J., & McGree, J. M. (2023). Gibbs optimal design of experiments.
University of Adelaide Barbara Kidman Fellowship 2023
- STATS 1005 Statistical Analysis and Modelling I (2022-2023)
- MATHS 1005 - Critical Evaluation in Data Science (2023
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