Below is a list of projects on the go at the Data Science Institute. While most projects pertain to the life sciences, from asthma to multiple sclerosis to seizure prediction, we are interested in taking on even more projects, both life sciences related and not. Using the analysis from these projects, we are able to find ways to visualize and present these data in the GDO. Contact us for more information.

Selected projects




U-BIOPRED: Unbiased BIOmarkers in PREDiction of respiratory disease outcomes


Using samples and medical information from hundreds of adults and children, the project aims to identify different sub-types of severe asthma.

Check out the U-BIOPRED website here.



European Translational Information and Knowledge Management Services (eTRIKS)

We are developing a sustainable open-source data and knowledge management platform to support translational research. TranSMART, an open-source knowledge management platform combining a data repository with a suite of search and analysis tools, is a core component of the eTRIKS platform development.

See the eTRIKS website here.

Check out the tranSMART foundation here


Deep Learning on Fused Brain

Deep Learning on Fused Brain and Heart Signals for Mental Disorder Detection

We are developing a patient-specific seizure prediction and detection system by investigating the use of fused brain and heart signals.

Check out the GitHub code here.


Managing Air for Green Inner Cities project has the aim to develop citites with no air pollution and no urban heat. They have plans to use advanced techniques for data assimilation, sensor optimization, and reduced order modelling for urban flow. 

Check out the website here.

Foundations of Data Science: Analytics

Advanced Data Analytics

Analytics is at the core of data science. Analytics research focuses on understanding data in terms of statistical models. It is concerned with the collection, analysis and interpretation of data, as well as the effective organisation, presentation and communication of results relying on data. At the Institute, advanced analytics and its applications are the main research focuses. Our work includesapplications to translational medicine, neuroscience, sensor informatics, urban informatics, human behavioural analysis, and social network analysis. We are particularly focusing on the real applications where learning processess take place in real-time and need to be adaptive to changing conditions.

Examples include deep learning for neurological diseases (epilepsy, multiple sclerosis) and compressive-sensing for functional MRI (fMRI) medical imaging analysis.

Data Economy

The essence of the big data revolution comes from the realisation that data is a new form of natural resource. Markets are emerging in which datasets are sold and traded as commodities. Large public datasets are available through government open-source websites such as the US-based or UK-based In both the private and public sectors, data science is the catalyst for advances in the physical sciences including: astrophysics, particle physics, biology, climate science and meteorology; the human sciences including medicine and healthcare; and the social sciences such as economics, business, and finance, as well as sociology, and political science. The generation of data in each of these fields offers businesses, governments, and non-profit organisations unparalleled opportunities to divine new insights into human behaviour and social dynamics.

At the Data Science Institute, we are carrying out research into the economic, legal and policy mechanisms required for the emerging Data Economy in the UK and worldwide. Working with our colleagues in economics and social science, we are pioneering in the key areas of data economy such as open data business model, digital money and digital service exchanges.

Security and Ethics of Data

Setting aside these practical issues in the data economy, consumer advocates understandably worry about treating data as an asset. Much of the anxiety associated with data protection, concerns not only how datasets are collected, maintained, disseminated, and destroyed (i.e. the security issues), but also how personal data is anonymised when used in the interest of public welfare, such as medical research. Technologies, such as anonymisation and pseudonymisation have been developed to address the ethics challenge.

At the DSI, we have built a strong research agenda in data security and data ethics research in collaboration with technical research groups (e,g security research in Department of Computing), policy expertise (e.g. Institute for Security Science and Technology) and the legal community. This research becomes even more crucial in our biomedical applications.