My focus is on using and developing new statistical and computational methods that can help to solve complex problems in the real world. These problems are in the fields of environment, genetics, health and medicine, and industry. My research areas include Bayesian statistics, hierarchical modelling and complex systems. What’s best about my job? That I can work with a diverse range of people doing outstanding things in many different areas, and contribute expertise in an important component of their work: making the best use of their data to help them make better decisions.
ACEMS Deputy Director Kerrie Mengersen is leading a team of researchers to try to save the jaguar population in Peru. Her project combines the use of statistics, mathematics, and virtual reality technology to help leaders make informed decisions about the threatened species, and the land it depends on.
Water-quality sensors are exposed to changing environments and extreme weather conditions and thus are prone to errors, including failure. These technical errors make data unreliable and untrustworthy and affect performance of any subsequent data analysis. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a feature based procedure, named oddwater, for detecting technical outliers in water-quality data derived from in situ sensors.
Bayesian methods for modelling and analysis of data are now well established. However, as with many statistical methods, their applicability in the context ‘big data’ is still being explored. This project will consider four questions that relate to ‘scalable Bayes’, that is, Bayesian models and computational methods that scale up to big data problems.
This project links to a number of other projects across the Centre.
ACEMS research combines state-of-art methodologies for coral reef data collection, remote sensing and statistical modelling to predict the future ecological status of the reefs within the Great Barrier Reef as the incidence of multiple disturbances continues to increase.
Dengue fever is one of the most serious health issues in the Asia-Pacific region, affecting more than 100-million people every year. ACEMS Researchers carried out a systematic review of the different spatial and spatio-temporal models that are looked into the disease.
Enormous effort is invested in monitoring the Great Barrier Reef (GBR), but data collection is currently fragmented over dozens of publicly and privately funded organisations, with data collected using different methods and for different purposes. As a result, the data are rarely analysed together.
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.
Prizes, awards and special recognition
Distinguished Professor was awarded to Kerrie Mengersen. Awarded from the Queensland University of Technology.
Research Excellence Award was awarded to Kerrie Mengersen. Awarded from the Cooperative Research Centre for Spatial Information.
Pitman Medal was awarded to Kerrie Mengersen. Awarded from the Statistical Society of Australia.
Spatial Enablement Award, Queensland Asia-Pacific Spatial Excellence Awards was awarded to Susanna Cramb, Kerrie Mengersen, Earl Duncan. Awarded from the The Spatial Industries Business Association (SIBA) and Geospatial Information & Technology Association ANZ (GITA) .
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.
Aswi, A.., Mengersen KL., Cramb S., Hu W., & White G.
(2018). Comparing Spatio-Temporal models using CARBayes: An application to dengue fever in Makassar, Indonesia. The joint international society for clinical Biostatistics and Australian Statistical Conference 2018.
Jahan, F., & Mengersen KL.
(2018). Bayesian Empirical Likelihood Spatial Model applying Leroux structure. International Conference on Bayesian Statistics in the Big Data Era Bayesian statistics in the era of Big Data .
Ebert, A., Wu P., Mengersen KL., & Ruggeri F.
(2018). A Review of Distances on Functional Datasets for Likelihood-Free Inference. Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.
Johnson, S., Logan M., Fox D., Kirkwood J., Pinto U., & Mengersen KL.
(2017). Environmental decision-making using Bayesian networks: Creating an environmental report card. Applied Stochastic Models in Business and Industry. doi: 10.1002/asmb.2190
Thomas, R., Walland M., Thomas A., & Mengersen KL.
(2016). Lowering of intraocular pressure after phacoemulsification in primary open-angle and angle-closure glaucoma. Asia-Pacific Journal of Ophthalmology. 5(1), 79-84. doi: 10.1097/APO.0000000000000174
Gonzalez, L., Montes G., Puig E., Johnson S., Mengersen KL., & Gaston K.
(2016). Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors. 16(1), doi: 10.3390/s16010097
Thomas, R., Mengersen KL., Thomas A., & Walland M. J.
(2016). Association between location of laser iridotomy and frequency of visual symptoms: a Bayesian learning analysis. Clinical & Experimental Ophthalmology. 44(3), 215-217. doi: 10.1111/ceo.12667
Xie, G., Roiko A., Stratton H., Lemckert C., Dunn P. K., & Mengersen KL.
(2016). A generalized QMRA beta-Poisson dose-response model. Risk Analysis. 36(10), 1948-1958. doi: 10.1111/risa.12561
White, N., & Mengersen KL.
(2016). Predicting health programme participation: A gravity-based, hierarchical modelling approach. Journal of the Royal Statistical Society: Series C (Applied Statistics). 65(1), 145-166. doi: 10.1111/rssc.12111
Reddan, T., Corness J., Mengersen KL., & Harden F.
(2016). Ultrasound of paediatric appendicitis and its secondary sonographic signs: Providing a more meaningful finding. Journal of Medical Radiation Sciences. 63(1), 59-66. doi: 10.1002/jmrs.154
Wells, J. A., Wilson K. A., Abram N., Nunn M., Gaveau D. L. A., Runting R. K., et al.
(2016). Rising floodwaters: Mapping impacts and perceptions of flooding in Indonesian Borneo. Environmental Research Letters. 11(6), doi: 10.1088/1748-9326/11/6/064016
Ashcroft, M. B., Casanova-Katny A., Mengersen KL., Rosenstiel T. N. A., Turnbull J. D., Wasley J., et al.
(2016). Bayesian methods for comparing species physiological and ecological response curves. Ecological Informatics. 34, 35-43. doi: 10.1016/j.ecoinf.2016.03.001
Hargrave, C., Mason N., Guidi R., Miller J-A., Becker J., Moores M., et al.
(2016). Automated replication of cone beam CT-guided treatments in the Pinnacle treatment planning system for adaptive radiotherapy. Journal of Medical Radiation Sciences. 63(1), 48-58. doi: 10.1002/jmrs.141
Herschtal, A., Foroudi F., Kron T., & Mengersen KL.
(2015). A Comparison of Bayesian Models of Heteroscedasticity in Nested Normal Data. Communications in Statistics - Simulation and Computation. 45(8), 2947-2964. doi: 10.1080/03610918.2014.936467
Fisher, R., O’Leary R. A., Low-Choy S., Mengersen KL., Knowlton N., Brainard R. E., et al.
(2015). Species Richness on Coral Reefs and the Pursuit of Convergent Global Estimates. Current Biology. 25(4), 500-505. doi: 10.1016/j.cub.2014.12.022
Hu, W., Zhang W., Huang X., Clements A., Mengersen KL., & Tong S.
(2015). Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis. Environmental Research. 136, 405-412. doi: 10.1016/j.envres.2014.07.033
Buys, L., Vine D., Ledwich G., Bell J., Mengersen KL., Morris P., et al.
(2015). A Framework for Understanding and Generating Integrated Solutions for Residential Peak Energy Demand. PLOS ONE. 10(3), doi: 10.1371/journal.pone.0121195
Nakagawa, S., Poulin R., Mengersen KL., Reinhold K., Engqvist L., Lagisz M., et al.
(2015). Meta-analysis of variation: ecological and evolutionary applications and beyond. Methods in Ecology and Evolution. 6(2), 143-152. doi: 10.1111/2041-210X.12309
Falk, M. G., Alston C. L., McGrory C. A., Clifford S., Heron E. A., Leonte D., et al.
(2015). Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling. Environmental and Ecological Statistics. 22(3), 571-600. doi: 10.1007/s10651-015-0311-1
Brown, E., Owen R., Harden F., Mengersen KL., Oestreich K., Houghton W., et al.
(2015). Predicting the need for adaptive radiotherapy in head and neck cancer. Radiotherapy and Oncology. 116(1), 57-63. doi: 10.1016/j.radonc.2015.06.025
Herschtal, A., L Marvelde te., Mengersen KL., Hosseinifard Z., Foroudi F., Devereux T., et al.
(2015). Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors. Physics in Medicine and Biology. 60(5), 1793-1805. doi: 10.1088/0031-9155/60/5/1793
Arbel, J., King C.K., Raymond B., Winsley T., & Mengersen KL.
(2015). Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel-contaminated soil. Ecology and Evolution. 5(13), 2633-2645. doi: 10.1002/ece3.1493
Herschtal, A., Marvelde L. Te, Mengersen KL., Foroudi F., Eade T., Pham D., et al.
(2015). Sparing Healthy Tissue and Increasing Tumor Dose Using Bayesian Modeling of Geometric Uncertainties for Planning Target Volume Personalization. International Journal of Radiation Oncology*Biology*Physics. 92(2), 446-452. doi: 10.1016/j.ijrobp.2015.01.034
Abram, N., Meijaard E., Wells J. A., Ancrenaz M., Pellier AS., Runting R. K., et al.
(2015). Mapping perceptions of species' threats and population trends to inform conservation efforts: the Bornean orangutan case study. Diversity and Distributions. 21(5), 487-499. doi: 10.1111/ddi.12286
Banu, S., Guo Y., Hu W., Dale P., Mackenzie J. S., Mengersen KL., et al.
(2015). Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh. Scientific Reports. 5, 16105. doi: 10.1038/srep16105
Kang, S. Yun, McGree J., Baade P. D., & Mengersen KL.
(2015). A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models. Australian & New Zealand Journal of Statistics. 57(3), 325-345. doi: 10.1111/anzs.12127
Wu, P., Pitchforth J., & Mengersen KL.
(2014). A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling. Transportation Research Part C: Emerging Technologies. 46, 247-260. doi: 10.1016/j.trc.2014.05.005
Yu, W., Mengersen KL., Dale P., Ye X., Guo Y., Turner L., et al.
(2014). Projecting Future Transmission of Malaria Under Climate Change Scenarios: Challenges and Research Needs. Critical Reviews in Environmental Science and Technology. 45(7), 777-811. doi: 10.1080/10643389.2013.852392
Davis, J., Mengersen KL., Bennett S., & Mazerolle L.
(2014). Viewing systematic reviews and meta-analysis in social research through different lenses. SpringerPlus. 3(1), 511. doi: 10.1186/2193-1801-3-511
Mellin, C., Mengersen KL., Bradshaw C. J. A., & M. Caley J.
(2014). Generalizing the use of geographical weights in biodiversity modelling. Global Ecology and Biogeography. 23(11), 1314-1323. doi: 10.1111/geb.12203