Main knowledge contributions towards intelligent data analytics fall into a range of areas including:
- Knowledge discovery in AI for healthcare to support patient self-management of general health and chronic conditions, involving smart monitoring, data validation from heterogeneous sensors, personal activity and event recognition, health information recommendation, personal health status estimation and serious gaming.
- Intelligent data analytics for computational creativity in AI by coordinating the EC-funded Dr Inventor research project and leading the development of the Dr Inventor platform. The Dr Inventor surrogate acts as a personal research assistant, utilising machine-empowered search and computation to bring researchers extended perspectives for scientific innovation by informing them of a broad spectrum of relevant research concepts and approaches, by assessing the novelty of research ideas, and by offering suggestions of new concepts and workflows with unexpected features for new scientific discovery.
- Visualization and parallel computing (GPU) for large-scale medical data, , including transfer function for feature enhancement in volume rendering of medical data; viewpoint selection and lighting design for volume rendering of medical data; Non-photorealistic volume rendering for feature enhancement from medical data; GPU-based iso-surface extraction from volume data and automated GPU-based parallelisation for images operations and image feature extractions.
- Visual analytics for health data to support the navigation, query and understanding of health records, clinical driven research in predictive models for cancer growth in response to treatment options, and the discovery of data patterns within patient cohort in both clinical and lifestyle domains
- Computer vision and machine learning for computer graphics research, including sparse modelling and representation for human motions, blind motion deblur for natural images, adaptive texture synthesis for high fidelity images and image based rendering based on inferences in machine learning
- Health data interoperability to support long-term collection of personal health information by aggregating electronic and personal health records, lifestyle data and drug information in a decentralised approach to offer easy access to personal medical history, empower the patients, improve self-management, and facilitate clinical research with significant advantages in privacy, security, safety, transparency and data integrity.
The recent active research projects include:
REAMIT- The project proposes to adapt and apply existing innovative technology to food supply chains in NWE to reduce food waste and hence improve resource efficiency (Project Information: European Commission Interreg North-West Europe, €608,118 for the local institution, from 2019 to 2022.) - Role: Co– Investigator
Aquaculture 4.0 -- The project will bring together several cutting-edge digital technologies including sensor networks for online monitoring, diagnosis, control and optimisation of aquaculture production, 5G communication for low-latency, high data rate, real-time transmission of big data, internet-of- things (IoT) system for big data storage, analytics, modelling and model-based decision making. By integration of these digital technologies, the project will deliver a prototype system of precision Aquaculture 4.0, and demonstrate the economic, environmental and social benefits through pilot applications in China (Project Information: Innovate UK, over £223,209 for the local institution, Feb 2019 – Dec 2021) - Role: Co-Investigator