Data Science

The Data Science discipline conducts world-leading research and develops innovative and practical solutions for business, scientific and social applications in the realm of big data.

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For more information about working with us, please contact our discipline leader, Professor Helen Huang or meet our team.

Research themes

Data quality management
Improving data quality practices in organisations; Computational methods for data quality detection and repair; Includes business, spatio-temporal and social data.
Information resilience
Tacking multi-disciplinary problems with computational, social and business experts; Balancing responsible and agile information use; Data science, machine learning and AI.
Explainability in machine learning
Focusing on interpretable deep models in many data-rich fields; Novel deep learning approaches in health-related and material science projects.
Computer vision
Multiview geometry, 3D reconstruction, shape analysis, image segmentation; Computer vision, machine learning and pattern recognition; Applications in immunology, histopathology and microbiology; Computer vision, pattern recognition and biometrics; Digital pathology and security; Security and surveillance.
Trajectory computing
Large scale spatiotemporal data management and analytics; Applications in spatial information systems, transport systems, IoT and streaming sensory data analytics; Storage, exploration and analysis of big data and designing novel big data platforms.
Bias and fairness in AI models
Human-AI methods that aim at solving difficult problems at scale; Working with Facebook to develop new methods to detect fake news.
Machine learning and optimisation
Deep learning, neural networks and statistical learning; Evolutionary computation, metaheuristics and black-box optimisation.
Social and multimedia data analytics
Modelling complex data to achieve human-understandable machine intelligence; Developing AI powered technologies; Designing methods to collect, manage, index, analyse and search big multimedia data.
Energy system and market data analytics
Time series clustering and classification; Energy management and optimisation; and Application in customer modelling and energy market design.
Data mining and predictive analytics
Recommender systems and user modelling; Virtual assistants and chatbots; Graph mining and embedding; and Time series and sequential data analysis.
Responsible big data intelligence
Cloud-free decentralised machine learning; On-device artificial intelligence that are memory- and energy-efficient (TinyAI); and Deep learning for personalised smart health application.
AI applications based on multiple data sources and data fusion
Data privacy preservation for distributed data sets; Big data visualisation and dashboard technology; and Machine learning on imperfect data.
Micro artificial intelligence (MAI)
AI for Edge Computing: Deployment of AI models on small network enabled devices; Applying AI technics on sensor networks; AI in IoT (Internet of Things) with effectiveness and efficiency issues; and Applications of FL (Federated Learning) on cloud.

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