The design, modelling, simulation and construction of software systems can increase productivity and reliability, and lead to reusable, evolvable and maintainable software, which can be used for novel applications such as computer aided/assisted/managed teaching and learning environments, and novel database technologies.
The Software Engineering group investigates novel approaches to software development; for example component-based software development, using symmetry and symmetry-breaking in software design, and using patterns for analysis, design, implementation and evolution of software systems, with a view to achieving the benefits of high productivity, as well as reliable, reusable, evolvable and maintainable software. Software engineering tools, including programming languages and their IDEs, and novel applications such as persistent object stores, computer aided/assisted/managed teaching and learning environments, and novel database technologies.
Postgraduate Research Projects
Kung-kiu Lau projects
Liping Zhao projects
Electronic Health Records (EHRs) contain an increasing wealth of medical information. They have the potential to advance support clinical and medical research, improve health policies, ensure and empower patients' safety and improve the overall quality of healthcare.
Linked2Safety explores the Semantic Web and Linked Data to facilitate semantic interlinking of EHRs and clinical trials systems for gathering and sharing knowledge to support decision making in medical and clinical research.
The vision of Linked2Safety is to advance clinical practice and accelerate medical research by providing pharmaceutical companies, healthcare professionals and patients with an innovative, secure and semantics-based interoperability framework facilitating the efficient and homogenised access to anonymised distributed EHRs. The framework will enable integration of clinical care information in EHR with information in clinical trial systems, which will facilitate the early detection of patients' safety issues, the identification of adverse events and the identification of a suitable critical mass of patients to participate in small (Phases II and III) or larger scale (Phase IV) clinical trials.
Prof John Keane
John Keane is Professor of Data Engineering in the School of Computer Science and a Fellow of the BCS. He has worked with healthcare professionals to link medical data with clinical trials information, and with people with early dementia or established cognitive impairment to allow those users to become aware of changes in their cognitive performance. The cognitive function self-awareness system is an example of a wider class of emerging systems of interest that require adaptive sense making with user-centred feedback.
Research interests: Data analytics.