Research projects in Information Technology

A PhD scholarship is available as part of an exciting research collaboration between the Faculty of Information Technology (FIT), the Faculty of Medicine, Nursing and Health Sciences (MNHS) and indigenous communities in rural Victoria.

The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information that is vital to their lives, such as information on their entitlements, public services, health, education or work opportunities. Timely access to information is essential to perform many economic, social and leisure activities. In today’s digital age, information is more and more often provided in digital form.

Collaborative problem-solving (CPS) has widely been recognised as an essential skill for success in the 21st century. Because of this, many researchers have focused on trying to better understand CPS in efforts to find out when it is effective, when it is not, and how to make it a teachable skill. 

Nowadays more and more intelligence software solutions emerge in our daily life, for example the face recognition, smart voice assitants, and autonomous vehicle. As a type of data-driven solutions, intelligent components learn their decision logic from data corpus in an end-to-end manner and act as a black box. Without rigorous validation and verification, intelligent solutions are error-prone especially when deployed in the real world environment. To monitor, identify, mitigate and fix these defects becomes extremely important to ensure their service quality and user experience.

People with disabilities are excluded from the assistive technology creation process because the methods and tools that are used are inaccessible. This leads to missed opportunities to create more accessible technologies for everyone including assistive technologies. This project will engage people with disabilities in the technology creation process at many levels, from engagement activities, input into designs and creation of technology and the facilitation of independent making of assistive technologies.

Science is a public good. The benefits of knowledge are or should be available to everyone, but the way this knowledge is produce often responds to individual incentives [1]. Scientist are not only cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science.

Matching thermal spectrum face images against visible spectrum face images has received increased attention in the literature, due to its broad applications in the military, commercial, and law enforcement domains. Thermal emissions from the face images are less sensitive to changes in ambient lighting.

Critical work to the future of healthcare ... exploring the role of #FHIR in interoperability and #datascience

Also allowing exploration and usage of the #SMART on #FHIR software paradigm 

Involves working with various real world health services and health IT partners 

#digitalhealth #health #EMR #hospital #software

We are living in the era of the 4th industrial revolution through the use of cyber physical systems. Data Science has revolutionised the way we do things, including our practice in healthcare. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical laboratory testing environment for improvement of patient care is under-utilized. 

With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth of techniques it offers, making ML seem an excellent tool for any task that involves building a model from data. Nevertheless, ML makes an implicit overarching assumption that severely limits its applicability to a broad class of critical domains: the data owner is willing to disclose the data to the model builder/holder.