About Computer Science Research Centre (CSRC)

The GREENGAGE project is about engaging citizens - mobilising technology - delivering the green deal. The GREENGAGE project brings together technology providers (mobile apps, Copernicus remote sensing, web tools, blockchain, AI/ML, visualisation, wearable sensors, IoT, UX, software engineering), social science, environmental and urban planning experts from seventeen organisations across Europe to develop citizen observatories across four pilots (Bristol, Copenhagen, Noord Brabant and Turano-Gerace). It leverages citizens’ participation and quips them with innovative digital solutions that will transform citizens’ engagement and cities’ effectiveness in delivering European Green Deal objectives for carbon neutral cities and shaping their climate mitigation and adaptation policies. This project is a collaboration with the Centre for Sustainable Planning and Environments.

For further information, contact Professor Zaheer Khan.

The SACRO project addresses a major bottleneck in the process of delivering valuable research insights from confidential data such as medical records held in Trusted Research Environments (TREs). This project will produce a consolidated framework with a rigorous statistical basis that provides guidance for TREs to agree consistent, standard processes to assist in quality assurance. It will design and implement a semi-automated system for checks on common research outputs, with increasing levels of support for other types of output, such as Machine Learning models. Working with a range of different types of TRE in different sectors (for example, health and socioeconomic data), organisations (including academia, government and the private sector) and the public will ensure wide applicability. This project is a collaboration with the Data Research, Access and Governance Network and the Mathematics and Statistics Research Group.

For further information, please contact Professor Jim Smith.

The goal of project BioMeld is to design and manufacture a biohybrid catheter that would be small and flexible enough to gain access to hard-to-reach areas and deliver drugs on site. In order to achieve this, we will develop a modelling and simulation framework, test it and optimise it by developing a reconfigurable modular biohybrid machine (BHM catheter, and group the necessary manufacturing equipment into a biointelligent manufacturing cell (BIMC). This project is a collaboration with the Unconventional Computing Laboratory.

For further information, contact Dr Michail-Antisthenis Tsompanas.

This African agriculture knowledge transfer partnerships (KTP) aims to develop transformational innovation strategy in a scaling poultry business through data-enabled innovations and implementing IoT (Internet of Things) for whole farm integration, emphasising cost-saving and revenue boosting. A new strategy for exploring data-enabled innovations (DEI) for the development of chicken business enterprises (CBEs) can enormously amplify the business capabilities, leading to higher productivity and profitability by reducing feed wastage, chicken mortality and stunted growth.

This project will implement IoT-based farm management technologies for real time data capture and perform rigorous data analytics in an iterative fashion to generate precise predictions. These analytical predictions will be used to generate retrospective and preventive insights to enhance business capabilities, reduce feed waste, improve chicken survivability and growth, as well as boost product quality.

For further information, please contact Professor Kamran Munir.

The provision of legal services is in a period of dramatic and rapid transition, and Lyons Davidson has identified AI-based technology to streamline its delivery as a key strategic aim, enabling it to thrive by out-competing other players in both quality and costs of provision. This Knowledge Transfer Partnership project will address how to build and deploy systems that can learn predictive models from historical records and adapt rapidly in response to human expertise in a way that conventional Machine Learning (ML) approaches do not accommodate. The project will focus throughout on demonstrating that strict ethical considerations such as privacy-preservation apply to all parts of the resulting systems.

For further information, please contact Professor Jim Smith.