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Research projects in Information Technology

Displaying 31 - 40 of 193 projects.

Connected Cars: Computational Models for Time-Critical Safety Applications

Connected vehicles need to be aware of their surrounding environments. This is impossible without being dependent on many sensory inputs. Sensor data is continually collected and analysed, in real-time in order to perform time-critical and delay-sensitive actions. There are two major challenges 1) limited computational resources (processing power and memory) on cars, 2) transfer of large sensory data to the cloud may is not feasible.

SE4AI: Enabling Reliable Deep Learning via Static Code Analysis

There are two ways to improve the reliability of machine learning applications: (1) on the reliability of the machine learning model or algorithm and (2) on the reliability of the code implementing the application. This project will mainly focus on the latter case, for which our fellow researchers have not started exploiting it yet. This project hence aims at supporting developers to implement reliable machine learning applications, both at the development phase and release phase.

Supervisor: Dr Li Li

Science as a public good: improving how we do research with game theory and computation

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.

Cooperation between artificial agents and human subjects

The future of AI technology is an ecosystem of many artificial agents acting with autonomy on behalf of human subjects. Examples include, a plethora of self-driving cars, or hordes of automated trading bots in a market. In these scenarios, interactions between artificial agents and human decision makers are abundant [1]. The core of AI has so far focused on applications in which agents help humans (aligned interests), or agents completely oppose humans (zero-sum games).

PhD Scholarship: Visualising Global Encounters & First Nations Peoples (Practice Based)

This PhD scholarship is funded as an important collaboration between the Faculty of Information Technology and the ARC Laureate project Global Encounters and First Nations Peoples: 1000 years of Australian History, conducted by Professor Lynette Russell AM.

Supervisor: Dr Thomas Chandler

Explainable Thermal to Visible Image Translation

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.

Supervisor: Dr Cunjian Chen

Interoperability (using FHIR) in cutting-edge medical software systems

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

Supervisor: Chris Bain

Explainable and Robust Deep Causal Models for AI assisted Clinical Pathology

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. 

Supervisor: Dr Lizhen Qu

Privacy-Preserving Machine Learning

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.