Digitally Enabled Care Models of the Future

Resilience of different network applications and devices

Co-supervised by Associate Professor Carsten Rudolph and Professor Chris Bain

From monitoring health via a smartwatch to driving an autonomous car, big data collection is becoming more ingrained in daily life. Growing with this trend however, are risks to data integrity. In this project, PhD student Fariha Jaigirdir analyses data security at each stage of IoT data propagation to develop a solution based on data provenance. Critical in healthcare, this will reveal how much an end user, such as a doctor, can trust information presented to them.

Model Driven Engineering and Evolutionary Machine Learning Base Approach for Modelling, Designing, Generating and Testing eHealth Apps for Smartphones and Wearables

Co-supervised by Professor John Grundy, Dr Li Li, Dr Qinghua Lu (Data61) and Dr Houreigh Khalajzadeh

With 150,000+ apps available, eHealth software has become extremely popular. But for the majority of these apps, development is costly and input from industry professionals is limited. What's more, many perform poorly, struggle with disruptions and fail to integrate with other health systems and software. To address these shortcomings, PhD student Md. Shamsujjoha has proposed a software engineering scheme to enhance the creation, reliability, robustness and productivity of eHealth apps   and shape the work of future developers and researchers.