Stephanie's research focuses on applied statistics, in particular improving the application of machine learning techniques in the field of hydrology and water resources. Stephanie's career started as a water resources engineer, followed by an MSc in applied mathematics and a PhD in statistics, with the aim of improving the extraction and visualisation of meaningful information from the vast amount of water-related data that is currently collected.
Supervised and unsupervised learning techniques
PhD - Statistics, UNSW, Australia
MSc - Applied and Computational Mathematics, Oxford University, UK
Gill, J. S., Clark S., Kadatz M., & Gill J.
(2020). The association of pretransplant dialysis exposure with transplant failure is dependent on the state‐specific rate of dialysis mortality. American Journal of Transplantation. doi: 10.1111/ajt.15917
Clark, S., Sisson S., & Sharma A.
(2020). Tools for enhancing the application of self-organizing maps in water resources research and engineering. Advances in Water Resources - Special issue on Machine Learning. 143, 103676. doi: 10.1016/j.advwatres.2020.103676
Clark, S., Kadatz M., Gill J., & Gill J. S.
(2019). Access to Kidney Transplantation after a Failed First Kidney Transplant and Associations with Patient and Allograft Survival. Clinical Journal of the American Society of Nephrology. 14(8), 1228 - 1237. doi: 10.2215/CJN.01530219
Lan, J. H., Gjertson D., Zheng Y., Clark S., Investigators DKAF., Reed E. F., et al.
(2018). Clinical utility of complement-dependent C3d assay in kidney recipients presenting with late allograft dysfunction. American Journal of Transplantation. 18(12), 2934 - 2944. doi: 10.1111/ajt.2018.18.issue-12,10.1111/ajt.14871
Clark, S., Sarlin P., Sharma A., & Sisson S.
(2015). Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map. Ecological Informatics. 26, 192-202. doi: 10.1016/j.ecoinf.2014.05.012