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Hack-proofing data analytics: A Blockchain-inspired approach to disabling deceptive data analytics practices

“Lies, damned lies, and statistics”. The preceding well-known phrase captures the, possibly unacknowledged, dilemma faced by organisations who are increasingly reliant on the power of data analytics to make important decisions. ‘Big data analytics’ has demonstrated its ability to deliver positive outcomes to organisations, from exposing and (dis-)proving anecdotally-ridden wisdom (or myths) to delivering targeted and effective data-driven improvement recommendations to business operations. However, drawing parallels to the practices of ‘creative accounting’, insights extracted from data analytics can potentially be …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Constrained deep learning for medical image analysis

A new, exciting research project needs 2 postdoctoral fellowships and 3 PhD students to work on developing the next generation of neuroimaging diagnosis using MRI.This large $4.8M project involves:Australia’s premier research organisation, the CSIROQueensland University of TechnologyMaxwell+, a start-up developing AI for precision medicineI-MED, a large private radiology practice.The scholars based at QUT in Brisbane will develop new deep learning technologies for the analysis of brain MRI with the goal of predicting neurodegenerative diseases such as Alzheimer’s.This PhD project will …

Study level
PhD
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Responsible Process Analytics: Let’s Blind the Prying Eyes

Process analytics is a form of data analytics with a focus on extracting data-driven insights from sequences of time-ordered events. Traditionally, process analytics have primarily been used to analyse business processes (e.g., insurance claims handling process, purchase requisition process, and student enrolment process). Nevertheless, the prevalence of timestamped data produced by today’s IT systems has seen a wider application of process analytics to other domains, including industrial control systems (e.g., power plant) and computer network systems, with a goal of …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Visualising Stochastic Business Process Models

Process mining aims to derive information from historical behaviour of processes in organisations which has been recorded in event logs. Business analysts use process mining software to visualise logs and derive information and insights for managers. Ultimately, this information is used to improve processes to, for instance, optimise costs, time and/or the environment. Process mining is an exciting field with lots of opportunity for research and with many commercial solutions being offered. Business process models describe what can happen in …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Information Systems

Measuring soundmark temporal variations in ecoacoustic recordings

The QUT Ecoacoustics research group collects a massive amount of passively-recorded environmental audio data. The data, currently 93TB in size, constitutes more than 46 years of combined environmental monitoring. This audio data is analyzed so that ecologists may scale their observations of the environment.However, as with all data-intensive projects, the data is not perfect. One of our larger collections of data, collected from the Sturt desert, has been misdated. The result is that for large sub-sections of the data, audio …

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Data centre management and optimisation with improved energy efficiency

Data centres have been increasingly built to provide a wide range of data, network, and cloud services. The increasing demand in various services requires more and more data centre resources and consequently more and more energy is consumed in data centres. It is estimated that about 30% of the running cost of a data centre is the energy consumption. Therefore, it is significant to manage and optimize data centre resources and energy consumption in data centres.

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Smart gird communications

Smart grid is a new concept to design and operate power generation, transmission, distribution and other related systems in an integrated environment with emerging services. There are a number of challenges in smart grid systems. One of the challenges is low-latency communications for real-time smart grid applications in wide area networks (WANs) such as wide area control, neighbourhood area networks (NANs) such as real-time demand response, and home area networks (HANs) such as home energy scheduling.The existing standards for smart …

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

Time series modelling of athlete performance using wearable technologies

With elite sports becoming ever more competitive, coaches, athletes and sports scientists are looking to use data to maximise training outcomes for greater competitive performance.For the 2018 Commonwealth Games on the Gold Coast, a series of projects are being offered towards the development of new statistical and machine learning tools in cross-disciplinary collaboration with sports scientists and end users. The projects involve QUT, the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS) and the Queensland Academy of Sport …

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Benchmarking elite athletes using multilevel models

With elite sports becoming ever more competitive, coaches, athletes and sports scientists are looking to use data to maximise training outcomes for greater competitive performance.For the 2018 Commonwealth Games on the Gold Coast, a series of projects are being offered towards the development of new statistical and machine learning tools in cross-disciplinary collaboration with sports scientists and end users. The projects involve QUT, the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS) and the Queensland Academy of Sport …

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Longitudinal modelling and analysis of elite swimming performance

With elite sports becoming ever more competitive, coaches, athletes and sports scientists are looking to use data to maximise training outcomes for greater competitive performance.For the 2018 Commonwealth Games on the Gold Coast, a series of projects are being offered towards the development of new statistical and machine learning tools in cross-disciplinary collaboration with sports scientists and end users. The projects involve QUT, the ARC Centre of Excellence in Mathematical and Statistical frontiers (ACEMS) and the Queensland Academy of Sport …

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

Turning predictions into decisions: innovation to facilitate spatio-temporal decision-making in the agricultural sciences

As agriculture meets the digital age, we are faced with challenging decisions about when to sow, when and how much to fertilise and when to irrigate. With the aid of high performance computing, models that represent complex agricultural processes can be used to simulate a wide range of farming scenarios in space and through time. Coupled with other sources of information (e.g. measured data and expert information) the challenge becomes being able to quantify the uncertainties and interpret the outputs …

Study level
PhD
Faculty
Science and Engineering Faculty
Lead unit
School of Mathematical Sciences

QUT Arm Farm

Deep Learning has seen significant growth and improvements in computer vision systems over the last years. Our work at the Australian Centre for Robotic Vision is looking at how robotic manipulation tasks be improved by using visual feedback. A lot of activities at the intersection at robotics, vision and learning at QUT.

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
School of Electrical Engineering and Computer Science

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