Professor Rhema Vaithianathan

Rhema Vaithianathan is a Professor of Social Data Analytics at the Institute for Social Science Research at the University of Queensland. She is also Director of the Centre for Social Data Analytics at Auckland University of Technology, New Zealand, where she is a Professor of Economics. Rhema is recognised internationally for translational research that uses data analytics for social good. She has been working with health data and collaborating with hospitals and health policy agencies for over 25 years, including work on indigenous health and disparities.

Rhema has a strong interest in the use of predictive analytics to support decision making in health and human services. Her work in this areas includes building hospital readmission risk tools and leading the international research team that developed, and continues to refine, the Allegheny Family Screening Tool, a child welfare predictive risk modelling tool for Allegheny County, PA (United States). Rhema is currently working on a range of predictive risk modelling projects with US state and county agencies.

Rhema is also very interested in using linked administrative data and novel data to evaluate programmes that are designed to improve outcomes and reduce disparities for vulnerable populations. In New Zealand she has evaluated several major health and social services programmes. Recent studies using administrative data have explored the cumulative prevalence of maltreatment, rates of injury and mortality among children identified as being at high risk of maltreatment, and protective factors that may allow some children to ‘beat the odds’ of experiencing Adverse Childhood Experiences (ACEs).

Rhema is Director of the Singapore Life Panel, a large population-representative monthly survey run from Singapore Management University that looks at how well-prepared Singaporeans are for the demands and risks associated with ageing. She has held numerous research positions in Australia, Singapore and the United States, including a Harkness Fellowship at Harvard University.

Position

Associate Investigator

Disciplines

  • Data Science

Qualifications

Bachelor of Commerce, The University of Auckland
Master of Commerce, The University of Auckland
Doctor of Philosophy, The University of Auckland