Working as a data scientist at the Sydney Informatics Hub, I have been applying frequentist, machine learning and Bayesian statistical techniques to a variety of research projects to produce insightful answers from structured and unstructured data. Highlights include:

Health

• An analysis of the relative risk of discharge against medical advice within the Sydney Children’s Hospital Network over five years of historical admissions records and 250k admissions, which I presented at the Health Data Analytics conference in Brisbane, October 2017.

• Clinical studies at the Woolcock Institute into sleep disorders such as insomnia and how they can be monitored with actigraphy measurements (i.e. a fitness wristwatch).

• Creating a software tool to streamline the process of analyzing metabolites through High Pressure Liquid Chromatography Mass Spectroscopy (HPLC-MS) at the Charles Perkins Centre.

• Developing a software tool to enable researchers to calculate chemical concentrations and kinetics in complex biological reactions involved in cell differentiation in developing spinal cords.

Education

•Financial modelling for the NSW Department of Industry’s Smart and Skilled program for Vocational education and training, a program which allocates more than $600 million a year in subsidies for NSW students.

Social Science

• Correlating election results for the 2016 US presidential election and the 2016 UK Brexit election with demographics of each electoral region.

• Analysing crime data across NSW looking for spatiotemporal patterns which can be exploited to improve policing efficiency.

While partly taking on the role of university statistician (2018-19) I provided statistical assistance to researchers across the university including meta-analyses, survey analysis and statistical methods.

I have taught customized 2 to 5 day courses in statistical data analysis and machine learning both in R and Python, delivered at the University of Sydney, UTS, Macquarie University and Amazon.