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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
Science and Engineering Faculty

Predicting core temperature for emergency services workers wearing encapsulating clothing

Clothing worn by firefighters is vital for protecting the wearer from the threats associated with doing their job. However, the barrier it creates between the wearer and the environment, as well as the weight and bulk of the clothing, both impair body heat loss and increase metabolic rate leading to a warmer deep body temperature. Consequently, firefighters experience elevated cardiovascular and thermoregulatory strain that impairs their tolerance to work in the heat. Accurate measurement of deep body temperature is invasive …

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
Science and Engineering Faculty

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
Science and Engineering Faculty

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
Science and Engineering Faculty

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
Science and Engineering Faculty

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
Science and Engineering Faculty

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
Science and Engineering Faculty

Intelligent transport systems

With the advancement in technology, new sources of transportation data (including traffic and transit, among others) have emerged which can potentially facilitate and even revolutionise transport modelling and simulation.My research team focuses on various aspects of the exploitation of emerging data for network planning, operations, management and control. This includes:New insights in travel behaviour modelling, traffic state estimation and predictionTravel patterns and multimodal travel behaviourTransit network monitoring, modelling or controlApplication of data from connected and autonomous vehicleData driven operation of …

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

Comparison of models for multivariate data analysis

Determining useful models for multivariate data still remains a challenge. There are several classes of models now available, and it is currently unclear as to which class of models tend to provide a better fit to real data in practice.This project will explore several classes of multivariate models, implement them on real data sets and compare the fits.This project would be suitable for extending into an Honours thesis.

Study level
Honours
Faculty
Science and Engineering Faculty
Lead unit
Science and Engineering Faculty

Statistical method for adjusting medal tallies for Olympic games

There is often a lot of interest in the general community for viewing adjusting medal tallies to account for say population size, country wealth and number of athletes. However, most news articles use very basic approaches and don't consider potentially non-linear relationships between medal tallies and other variables. This project will develop a principled statistical method for performing the adjustment that more accurately model's dependencies in the data.

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
Lead unit
Science and Engineering Faculty

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
Science and Engineering Faculty

Opportunities and challenges faced in intelligent transport systems

With the advancement in technology, new sources of transportation data, including traffic and transit data, have emerged which can potentially facilitate and even revolutionise transport modelling and simulation.My research team focuses on various aspects of the exploitation of emerging data for network planning, operations, management and control. This includes:new insights in travel behaviour modelling, traffic state estimation and predictiontransit network monitoring, modelling or controlapplying data from connected and autonomous vehicledata-driven operation of transportation systems with artificial intelligence and machine learning …

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

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