School of
Information Technology and Electrical Engineering

PhD or MPhil in Data Science

Eligible students may undertake a MPhil or PhD within the field of Data Science, subject to the availability of appropriate supervisory staff.

Steps to take:

1. Check your eligibility

2. Contact and confirm your supervisor

For a full list of Data Science academic staff, please visit this link.
For a list of current research interests, please visit this link.

3. Prepare documents

4. Apply online

For further information on the application process, please contact the School of ITEE's HDR Officer.

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Data Science Research Highlights

Data science research spans a number of areas including social media analytics, big data infrastructure, machine learning and optimization, data quality management, multimedia search, data mining and recommender systems, computer vision and pattern recognition. See featured projects below.

For a full list of Data Science academic staff, please visit this link.
For a list of current research interests, please visit this link.

Trajectory Computing

Spatial and temporal information are ubiquitous to most data we have today. UQ Data Science is a world-leading group in large scale spatiotemporal data management and analytics.  Working with leading industry partners such as SAP, Microsoft, Neusoft and Queensland Government, we have developed advanced knowledge and techniques from data quality management, data storage and high performance processing, to data mining and machine learning for trend prediction for spatiotemporal data, with applications in Spatial Information Systems, Intelligent Transport Systems, IoT and streaming sensory data analytics.
Contact Xiaofang Zhou zxf@itee.uq.edu.au

Explainability in Machine Learning

Using state-of-the-art deep learning techniques, we are focusing on developments of interpretable deep models in many data-rich fields. In health-related projects, we pay attention not only to achieving high performance in medical prediction tasks, such as illness severity prediction in ICUs but also to addressing explainability issues raised by the “black-box” models. In another application, we are developing novel deep learning approaches to learn the existing alloy compositions, aiming to discover new compositions with the desirable properties, which may open a new avenue for material science.
Contact Sen Wang sen.wang@uq.edu.au

Social and Multimedia Data Analytics

Modelling large-scale, unstructured, multi-modal and complex data, achieving human-understandable machine intelligence for multimedia and social media analytics. We develop AI powered technologies to enable tangible benefits for the whole society, ranging from the applications such as autonomous vehicles, event detection and prediction, and aged care. This stream of work is being used at the Centre of Excellence for Children and Families over the Life Course.
Contact Helen Huang huang@itee.uq.edu.au

Bias and Fairness in AI Models

Human-AI methods that aim at solving difficult problems at scale. Addressed research problems include the use of humans in the loop to deal with bias, fairness, and transparency of AI models. Application domains include human-AI collaboration in the future of work and the use of humans to support the automatic identification of online misinformation at scale. We are working with organizations such as Facebook to develop new methods to detect fake news.
Contact Gianluca Demartini g.demartini@uq.edu.au

Digital Pathology

The aim of the digital pathology is to develop technologies that make pathology workflows much faster and more efficient. The research utilises computer vision, machine learning and pattern recognition methods to create digital pathology scanning systems as well as image-based Computer Aided Diagnostic (CAD) systems. Along with its' partners Sullivan Nicolaides Pathology, the researchers are continuously creating a number of computer image datasets from different pathology areas which will assist in further advancing the field.
Contact Brian Lovell lovell@itee.uq.edu.au

For a full list of Data Science academic staff, please visit this link.
For a list of current research interests, please visit this link.

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What is Data Science?

With the move towards an increasingly digitised future, the value of data science is rapidly growing.  Big data underpins our digital economy - it revolutionises the way we work, live and communicate. As data grows every day, there is significant and ongoing growth in the demand for Data Scientists and Researchers globally.

What can I do with a degree in Data Science?

Data Science research at UQ offers a combination of high-level technical skills and a capacity for creative and disruptive thinking. Graduates are ready to solve complex data science problems globally and meet the strong demand for expert data scientists.

What makes a good Data Scientist?

Distinctive skills and capacity for innovative thinking towards complex problems.

What are my career options?

Data Science has become one of the most sought after careers worldwide, and opens global employment and career opportunities. There have been numerous reports of massive skills shortage and the demand seems to be ever increasing as more and more businesses embrace the power of big data to gain competitive advantage. In fact the transformative power of data is recognised in many disciplines, not just business, such as biomedicine, cyber security, public health, social policy and so on. Data Science graduates are ideally positioned to pursue high end data scientist careers in Australia and globally across a large number of industry sectors, government agencies, and leading technology companies.