European researchers have made leading contributions to the large genomic, transcriptomic and clinical datasets from patients with chronic diseases. Advances in information science provide unprecedented opportunities for using these datasets to elucidate the complex biology of these disorders, its influence by environmental triggers, and to personalise their management. Currently, exploitation of these opportunities is limited by a shortage of researchers with the required informatics skills and knowledge of requisite data protection principles. HELICAL addresses this unmet need by developing a trans-sectoral and interdisciplinary training programme that builds on the expertise and existing collaborations of its partners. It provides 15 early stage researchers with training in analysis of large datasets, using autoimmune vasculitis as a paradigm as it is scalable, and as comprehensive biological and clinical datasets are already available. The HELICAL training program focuses on three complementary areas: application of informatics to such datasets to gain new biological insights; translation of these into practical clinical outputs and management of ethical constraints imposed on such studies. The programme will be delivered through a multidisciplinary, trans-sectoral partnership of Academic and Industry researchers with expertise in basic biomedical research, epidemiology, statistics, machine learning, health data governance and ethics. Therefore, HELICAL exploits recent advances in data science to link research datasets with longitudinal healthcare records, based on the robust ethical foundation required for linkage studies using near-patient data, to address key experimental questions. The results will have obvious potential for transforming healthcare in the field of autoimmune disease. The training provided addresses a key skills gap in the European workforce and should make the ESR eminently employable in academic, industrial and clinical sectors.

see main proposal

Within the next few years the number of devices connected to each other and the Internet will outnumber humans by almost 5:1. These connected devices will underpin everything from healthcare to transport to energy and manufacturing. At the same time, this growth is not just in the number or variety of devices, but also in the ways they communicate and share information with each other, building hyper-connected cyber-physical infrastructures that span most aspects of people's lives. For the UK to maximise the socio-economic benefits from this revolutionary change we need to address the myriad trust, identity, privacy and security issues raised by such large, interconnected infrastructures. Solutions to many of these issues have previously only been developed and tested on systems orders of magnitude less complex in the hope they would 'scale up'. However, the rapid development and implementation of hyper-connected infrastructures means that we need to address these challenges at scale since the issues and the complexity only become apparent when all the different elements are in place. There is already a shortage of highly skilled people to tackle these challenges in today's systems with latest estimates noting a shortfall of 1.8M by 2022. With an estimated 80Bn malicious scans and 780K records lost daily due to security and privacy breaches, there is an urgent need for future leaders capable of developing innovative solutions that will keep society one step ahead of malicious actors intent on compromising security, privacy and identity and hence eroding trust in infrastructures. The Centre for Doctoral Training (CDT) 'Trust, Identity, Privacy and Security - at scale' (TIPS-at-Scale) will tackle this by training a new generation of interdisciplinary research leaders. We will do this by educating PhD students in both the technical skills needed to study and analyse TIPS-at-scale, while simultaneously studying how to understand the challenges as fundamentally human too. The training involves close involvement with industry and practitioners who have played a key role in co-creating the programme and, uniquely, responsible innovation. The implementation of the training is novel due to its 'at scale' focus on TIPS that contextualises students' learning using relevant real-world, global problems revealed through project work, external speakers, industry/international internships/placements and masterclasses. The CDT will enrol ten students per year for a 4-year programme. The first year will involve a series of taught modules on the technical and human aspects of TIPS-at-scale. There will also be an introductory Induction Residential Week, and regular masterclasses by leading academics and industry figures, including delivery at industrial facilities. The students will also undertake placements in industry and research groups to gain hands-on understanding of TIPS-at-scale research problems. They will then continue working with stakeholders in industry, academia and government to develop a research proposal for their final three years, as well as undertake internships each year in industry and international research centres. Their interdisciplinary knowledge will continue to expand through masterclasses and they will develop a deep appreciation of real-world TIPS-at-scale issues through experimentation on state-of-the-art testbed facilities and labs at the universities of Bristol and Bath, industry and a city-wide testbed: Bristol-is-Open. Students will also work with innovation centres in Bath and Bristol to develop novel, interdisciplinary solutions to challenging TIPS-at-scale problems as part of Responsible Innovation Challenges. These and other mechanisms will ensure that TIPS-at-Scale graduates will lead the way in tackling the trust, identity, privacy and security challenges in future large, massively connected infrastructures and will do so in a way that considers wider sosocial responsibility.

In this proposal, we address the matter of transparency and explainability of AI using approaches inspired by control theory. Notably, we consider a comprehensive and flexible certification of properties of AI pipelines, certain closed-loops and more complicated interconnections. At one extreme, one could consider risk averse a priori guarantees via hard constraints on certain bias measures in the training process. At the other extreme, one could consider nuanced communication of the exact tradeoffs involved in AI pipeline choices and their effect on industrial and bias outcomes, post hoc. Both extremes offer little in terms of optimizing the pipeline and inflexibility in explaining the pipeline’s fairness-related qualities. Seeking the middle-ground, we suggest a priori certification of fairness-related qualities in AI pipelines via modular compositions of pre-processing, training, inference, and post-processing steps with certain properties. Furthermore, we present an extensive programme in explainability of fairness-related qualities. We seek to inform both the developer and the user thoroughly in regards to the possible algorithmic choices and their expected effects. Overall, this will effectively support the development of AI pipelines with guaranteed levels of performance, explained clearly. Three use cases (in Human Resources automation, Financial Technology, and Advertising) will be used to assess the effectiveness of our approaches.

The UK's healthcare system faces unprecedented challenges. We are the most obese nation in Europe and our ageing population is especially at risk from isolation, depression, strokes and fractures caused by falls in the home. UK health expenditure is already very substantial and it is difficult to imagine the NHS budget rising to meet the future needs of the UK's population. NHS staff are under particular pressure to reduce hospital bed-days by achieving earlier discharge after surgery. However this inevitably increases the risk that patients face post operative complications on returning home. Hospital readmission rates have in fact grown 20% since 1998. Many look to technology to mitigate these problems - in 2011 the Health Minister asserted that 80% of face-to-face interactions with the NHS are unnecessary. SPHERE envisages sensors, for example: 1) That employ video and motion analytics to predict falls and detect strokes so that help may be summoned. 2) That uses video sensing to analyse eating behaviour, including whether people are taking their prescribed medication. 3) That uses video to detect periods of depression or anxiety and intervene using a computer-based therapy. The SPHERE IRC will take a interdisciplinary approach to developing these sensor technologies, in order that: 1) They are acceptable in people's homes (this will be achieved by forming User Groups to assist in the technology design process, as well as experts in Ethics and User-Involvement who will explore issues of privacy and digital inclusion). 2) They solve real healthcare problems in a cost-effective way (this will be achieved by working with leading clinicians in Heart Surgery, Orthopaedics, Stroke and Parkinson's Disease, and recognised authorities on Depression and Obesity). 3) The IRC generates knowledge that will change clinical practice (this will be achieved by focusing on real-world technologies that can be shown working in a large number of local homes during the life of the project). The IRC "SPHERE" proposal has been developed from day one with clinicians, social workers and clinical scientists from internationally-recognised institutes including the Bristol Heart Institute, Southampton's Rehabilitation and Health Technologies Group, the NIHR Biomedical Research Unit in Nutrition, Diet and Lifestyle and the Orthopaedic Surgery Group at Southmead hospital in Bristol. This proposal further includes a local authority that is a UK leader in the field of "Smart Cities" (Bristol City Council), a local charity with an impressive track record of community-based technology pilots (Knowle West Media Centre) and a unique longitudinal study (the world-renowned Avon Longitudinal Study of Parents and Children (ALSPAC), a.k.a. "The Children of the Nineties"). SPHERE draws upon expertise from the UK's leading groups in Communications, Machine Vision, Cybernetics, Data Mining and Energy Harvesting, and from two corporations with world-class reputations for research and development (IBM, Toshiba).