Announcing our second round of data fellowships

8 June 2018

Seven more data specialists are taking up our highly coveted three-month fellowship placements. They’ll develop solutions to data-related problems and help to improve government services.

The DTA's 7 new data fellows
Caption: Our seven latest data fellows. Top row left to right: Todd Campbell, Gabriella Duddy, Dipangar Kundu, Daniel Merkas. Bottom row left to right: Jack Xu, Rakhesh Devadas, Patrick Drake-Brockman

We’re pleased to announce our second round of data fellows for 2017–18. We’ve picked seven talented people to take part in three-month placements under our Data Fellowship Program.

We’ve awarded the fellowships to high-performing data specialists in the Australian Public Service. During their placements they work with the CSIRO’s data-innovation group Data61 and will develop solutions for data-related problems or opportunities.

The fellowships give public service staff the chance to step outside their day jobs and create prototypes that improve or completely redesign our approach to real-life problems.

Participants come from a variety of government agencies. These fellowships give them access to mentoring, skills development and new and emerging technologies and techniques.

Here are our seven new data fellows and the projects they’re working on between now and mid-October 2018:

Checking financial condition reports

Todd Campbell

Australian Prudential Regulation Authority

Todd is looking at how to use text analytics to help identify insights and risks in prudential supervisory reviews of insurance companies’ financial condition reports. He’s also looking at identifying efficiencies and compiling an overview of the industry to assist supervisors.

These documents are currently reviewed manually to identify issues and feed into other risk assessments. Specialist teams also review the documents to help supervisors, and more systematic analysis could assist this process. 

Todd is responsible for delivering an Innovation Centre Lab for the Australian Prudential Regulation Authority.

The goal of the lab is to test modern analytics approaches that could provide new insights and support decisions relating to the authority’s prudential supervision role.

Automating land-use delineation

Rakhesh Devadas

Australian Bureau of Agricultural and Resource Economics and Sciences (Department of Agriculture and Water Resources)

Rakhesh is developing techniques to automate the way agricultural land uses are mapped at a national scale.

His project will also improve methods for building the Land Use of Australia data series for understanding current and long-term changes in Australian land use.

He’s doing this by developing advanced geospatial data-analysis and modelling techniques. These techniques will combine satellite-derived information and various national datasets. 

Rakhesh has post-graduate qualifications in agricultural economics and a doctorate in spatial data applications.

His 16 years’ of professional experience include implementing operational projects for the NSW and Queensland state governments and research projects involving satellite time-series data at RMIT University in Melbourne and University of Technology Sydney.

Analysing government buying patterns

Patrick Drake-Brockman

Digital Transformation Agency

Patrick’s project will provide data about the way government agencies buy products and services and the sellers they buy from.

He’s using longitudinal network analysis to look at datasets held by AusTender, the website where many contract details are posted. This analysis will show the effects policy changes have on forming networks between government agencies and sellers.

Patrick is a senior adviser in the Digital Transformation Agency’s investment office, providing advice to the government on ICT procurement proposals.

He’s spent 19 years in the Australian Government in roles including ministerial ICT support, critical infrastructure policy and national security information sharing policy.

Detecting non-compliance in regulatory schemes

Gabriella Duddy

Clean Energy Regulator

Gabriella is using machine learning to help the Clean Energy Regulator (CER) detect non-compliance in its regulatory schemes.

She’s developing a process for the Small-Scale Renewable Energy Scheme to help the CER to use its resources more efficiently and strengthen the integrity of Australia’s Renewable Energy Target.

Gabriella has worked across the CER’s intelligence and analytics functions, helping to develop the agency’s approach to increasing data capability.

She’s in her final semester of a Master of Energy Change at the Australian National University in Canberra.

Targeting biosecurity risks at airports

Dipangkar Kundu

Department of Agriculture and Water Resources

Dipangkar is developing an empirical model for identifying and targeting potential non-compliance biosecurity risks at Australian airports.

His model will help to find risk patterns and identify international travellers who carry a higher biosecurity risk.

Dipangkar is a senior data analyst at the Department of Agriculture and Water Resources, where he was honoured with the Secretary Award of Achievement for his contribution to biosecurity risk profiling.

He has a PhD in computational hydrology from the University of Sydney.

Improving survey accuracy

Daniel Merkas 

Australian Bureau of Statistics

Daniel is developing machine-learning models to improve the quality and efficiency of the address register held by the Australian Bureau of Statistics.

The register contains more than 10 million addresses and helps to improve surveys and link datasets to inform Australia’s important decisions.

He’ll use machine-learning algorithms to automate decisions that currently need people to carry them out and are resource-intensive.

Daniel is a statistician in the bureau’s address register unit and is studying for a Master of Science (Applied Statistics) at Swinburne University.

Simulation model for call-centre activities

Jack Xu

Department of Human Services

Jack is building a simulation model that will replicate the day-to-day telephone activities of the Department of Human Services.

He plans to show information including customer wait times, number of transferred calls, busy signals, level of staff occupancy and more. Jack’s model will enable his team to answer important business questions with more confidence than ever before. 

Jack is a data analyst whose job is to look for ways DHS operational staff can be more efficient in their work. This includes helping to ensure the department achieves its telephone and claims processing deliverables.

He previously worked at analytics software firm SAS Institute and holds a Bachelor of Actuarial Studies.

Find out more about our Data Fellowship Program.

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