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Our data science team includes experienced industry data science professionals and industrial PhD candidates who are undertaking research across data science, machine learning and AI disciplines. 
This gives us a unique ability to apply the most current research and also draw on our proven commercial experience to deliver data science solutions in a range of real-world environments.
 
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Analytics to derive insights from your data;
Consulting advice to identify optimisation opportunities, facilitate knowledge transfer and develop strategic solutions to address emerging trends and patterns; 
Managed analytics environment to facilitate efficient access and drive tangible business outcomes.  

Data Science is a complex and ever evolving field, and we believe that the best way to deliver an appropriate solution is to combine our research and industry knowledge to deliver the most appropriate methods and platforms. 

Working in this way allows us to build from a small POC right through to a production grade system ensuring that the solution:

  • Is appropriately sized, not a one size fits all solution
  • Can scale cost efficiently
  • Addresses directly the most material use cases
  • Is built with the specific clients use cases in mind
  • Is built by data science experts who have a deep understanding of complex machine learning and AI methods and when and where to deploy them appropriately.

Transport Accident Commission 
 

The Transport Accident Commission (TAC) of the Victorian Government is a large state agency tasked with the compensation and support of victims of transportation accidents.

From 2016 to 2019, Prof. Piccardi, PhD student Sattar Seifollahi and other team members carried out a collaborative project aimed to "make sense" of the large amount of textual data owned by the TAC.

The project developed incrementally over a number of business cases raised in turn by the management and the internal data science team led by Mr. Bernie Kruger. Funding and governance were provisioned by the TAC and a Cooperative Research Centre (the CMCRC Healthcare Market Quality program) of which the TAC was a member.

The project delivered:

An NLP-based approach to predict the return-to-work of TAC clients based on the automated analysis of transcripts of early phone calls between the clients and case managers.

The project analysed over 100,000 phone call transcripts to extract the dominant conversational topics of cohorts of clients with different return-to-work status. Using the topic vectors extracted from the phone call transcripts of the early stages of the compensation claim (first 3 months from accident), it has been possible to predict the clients who returned to work within the expected time frame (18-24 months) with approximately 85% accuracy.

A computational topic model aimed to automatically detect mental and blame issues in the phone calls between TAC clients and case managers. The model has been used to support understanding and planning for the clients’ return to work.

An automated taxonomy of a large collection of TAC documents built to facilitate browsing and search of claim documents by the TAC case managers. The taxonomy was based on word embeddings, hierarchical clustering and a dedicated "vocabulary" containing all the TAC's common acronyms and terminology. The taxonomy was eventually integrated into a Claim Search tool for mobile devices using Shiny.
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