We are living in an era of Big data with the rapid technological developments in information technologies and communications providing an unprecedented amount of data and new forms of data. Big data is now an integral part of our daily lives and are routinely produced by local government and business. In these settings, data production is just a by-product of the activities local government or business are involved in: most often, this information is collected for a specific purpose but very little use is made of these data-sets beyond the original purpose they were designed for. The challenge is how we can make better use of these types of information to improve our quality of life and foster economic growth. If combined together, these datasets can provide valuable information and insights into how businesses and local authorities work, the ways in which improvements to services can be made or businesses become more successful and efficient in their operation. Big data can provide local authorities and businesses additional information which can help them to design better policies and improve their business operations. To date, very little data of this type has been available for social scientific research in a systematic way. The aim of the new Smart Data Analytics (SDA) for Business and Local Government research centre is to utilise this explosion of information for social scientific research to answer questions that affect all our lives. For example, in an era of austerity and belt-tightening for local authorities, how can they make best use of limited resources to deliver the highest quality service to residents including across health and social care provision, education, crime reduction, housing and transport? By using data sources collected by local authorities for their administrative purposes we can start to unravel some of these questions and make relevant and timely policy recommendations. We have partnered with three local councils in Kent, Essex and Norfolk who are keen to work with academic researchers to learn from the information they hold to improve their service delivery but at present do not fully utilise. We have also partnered with businesses who wish to understand how we can foster and support economic growth, particularly for small and medium enterprises and start-ups. What are the barriers these businesses face and how can Big data help us understand the best means of overcoming these? The SDA will establish a secure data facility at the University of Essex where Big data from a variety of sources are stored and matched so to produce new information which can be useful to both local authorities and businesses. At the same time, the facility will give researchers, local authorities and businesses a point of access to Big data and expertise and support in using those data. There are clearly many issues of data privacy and confidentiality to be considered and the Centre will develop safe methods of handling, anonymising and linking data to ensure the confidentiality of businesses and individuals is maintained and respected. The Centre will also carry out research into how Big data can best be analysed as some of the methods used for more standard forms of data such as social surveys may not apply. We have an innovative substantive research programme articulated in a set of research streams designed to focus on key policy issues: (i) Methodological advances in Big Data analysis; (ii) Local economic growth, (iii) Support for vulnerable people; and (iv) the Green Infrastructure. The Centre will also provide training and support to new researchers, businesses and local authorities and engage actively with both businesses and local authorities through tailored knowledge exchange activities which will draw on the expertise built in the Centre. The new Centre promises to be an exciting development that will not only advance knowledge but have a positive impact on our quality of life.

Machine learning (ML), in particular Deep Learning (DL) is one of the fastest growing areas of modern science and technology, which has potentially enormous and transformative impact on all areas of our life. The applications of DL embrace many disciplines such as (bio-)medical sciences, computer vision, the physical sciences, the social sciences, speech recognition, gaming, music and finance. DL based algorithms are now used to play chess and GO at the highest level, diagnose illness, drive cars, recruit staff and even make legal judgements. The possible applications in the future are almost unlimited. Perhaps DL methods will be used in the future to predict the weather and climate, of even human behaviour. However, alongside this explosive growth has been a concern that there is a lack of explainability behind DL and the way that DL based algorithms make their decisions. This leads to a lack of trustworthiness in the use of the algorithms. A reason for this is that the huge successes of deep learning is not well understood, the results are mysterious, and there is a lack of a clear link between the data training DL algorithms (which is often vague and unstructured) and the decisions made by these algorithms. Part of the reason for this is that DL has advanced so fast, that there is a lack of understanding of its foundations. According to the leading computer scientist Ali Rahimi at NIPS 2017: 'We say things like "machine learning is the new electricity". I'd like to offer another analogy. Machine learning has become alchemy!' Indeed, despite the roots of ML lying in mathematics, statistics and computer science there currently is hardly any rigorous mathematical theory for the setup, training and application performance of deep neural networks. We urgently need the opportunity to change machine learning from alchemy into science. This programme grant aims to rise to this challenge, and, by doing so, to unlock the future potential of artificial intelligence. It aims to put deep learning onto a firm mathematical basis, and will combine theory, modelling, data, computation to unlock the next generation of deep learning. The grant will comprise an interlocked set of work packages aimed to address both the theoretical development of DL (so that it becomes explainable) and the algorithmic development (so that it becomes trustworthy). These will then be linked to the development of DL in a number of key application areas including image processing, partial differential equations and environmental problems. For example we will explore the question of whether it is possible to use DL based algorithms to forecast the weather and climate faster and more accurately than the existing physics based algorithms. The investigators on the grant will be doing both theoretical investigations and will work with end-users of DL in many application areas. Mindful that policy makers are trying to address the many issues raised by DL, the investigators will also reach out to them through a series of workshops and conferences. The results of the work will also be presented to the public at science festivals and other open events.

In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future. Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK. Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.

The uneven ways that civil liberties, work, labour and health have all been impacted over the last 18 months as we have all turned to digital technologies to sustain previous ways of life, has not only shown us the extent of inequalities across all societies as they are cut through with gender, ethnicity, age, opportunities, class, geolocation; it has also led many organisations and businesses across all three sectors to question those values they previously supported. Capitalising on this moment of reflection across industry, the public and third sectors; we explore the possibility of imagining and building a future that takes different core values and practices as central, and works in very different ways. As the roles of organisations and businesses across all industry, the public and third sectors changes, what is now taken up as core values and ethos will be crucial in defining the future. INCLUDE+ will build a knowledge community around in/equalities in digital society that will comprise industry, academia, the public and third sectors. Responding to the Equitable Digital Society theme, we ask how we can design, co-create and realise digital services and infrastructures to support inclusion and equality in ways that enable all people to thrive. Focusing on the three connected strands of wellbeing, precarity, and civic culture; we address structural inequalities as they emerge through our research, investigating them through whole system approaches that includes the generation of outputs that comprise of new systems, services and practices to be taken up by organisations. More than this, our knowledge community will be underpinned by empirical, co-curation and participatory led research that will produce real interventions into those structural inequalities. These interventions will be taken up by organisations, responded to and considered, enabling the wider knowledge community to critically assess them in relation to the values they purport to promote. Fed by secondments and supported through smaller exploratory and escalator funds, our knowledge community will not only grow through traditional networking activities such as workshops, annual conferences, academic outputs and further funding; it will also grow through the development of interdisciplinary methods, knowledge exchange practices, and mentorship, which the secondment package will promote. In so doing, we structure our N+ around participatory research practices, people development and knowledge exchange, aiming to grow our network through the development and growth of people and good practice. INCLUDE+ is led by a highly experienced cross-disciplinary team incorporating Management and Business Studies, Computing, Social Sciences, Media and Communication and Legal Studies. Each Investigator brings vibrant international networks; active research projects feeding the Network+; and long experience of impact generation across policy and research. With support from organisations like the International Labour Organisation, Law Commission, Cabinet Office, and Equality and Human Rights Commission as well as the existing DE community, we will develop from and with existing research, extend this work and impact beyond it. Our partner organisations cut across industry, the public and third sectors and include (for example) Lego; NHS AI Lab; Space2; mHabitat; Leeds, Cambridgeshire and Swansea Councils; PeopleDotCom; Ditchley; 5Rights; EAMA; DataKind and IBM. We have designed the Network+ to enable a whole system approach that is genuinely exciting and innovative not just because of scalability, transference and scope, but also because of the commitment to people development, knowledge exchange and interdisciplinary practice that will also shape future research

Climate and environmental (CE) risks (CER) to our economy and society are accelerating. CER include climate-related physical risks such as floods, storms, or changing growing seasons; climate-related transition risks such as carbon pricing and climate litigation; and environmental risks such as biodiversity loss. It is now well accepted that CER can impact asset values across multiple sectors and pose a threat to the solvency of financial institutions (FIs). This can cause cascading effects with the potential to undermine financial stability. The adoption of CER analytics will ensure that CE risks can be properly measured, priced, and managed by individual FIs and across the financial system. This is also a necessary condition to ensure that capital is allocated by FIs towards technologies, infrastructure, and business models that lower CER, which are also those required to deliver the net zero carbon transition, climate resilience, and sustainable development. These twin tracks - greening finance and financing green - are both enabled by CER analytics being appropriately used by FIs. The UK is a world-leader in Green Finance (GF). UK FIs have played a key role in GF innovation. Yet, despite these advances and leadership in almost every aspect of GF, UK FIs cannot secure the data and analytics needed to properly measure and manage their exposures to CER. While the last decade has seen the exponential growth of CE data, as well as improved analytics and methods, often produced by world-leading UK science, the vast majority of this has not found its way into FI decision-making. Our vision for CERAF is to establish a new national centre to resolve this disconnect. CERAF aims to enable a step-change in the provision and accessibility of data, analytics, and guidance and accelerate the integration of CER into products and decisions by FIs to manage CER risks and drive efficient and sustainable investment decisions, thereby delivering the following impacts: - Enhance the solvency of individual FIs in the UK and globally and so contribute to the resilience of the global financial system as a whole for all, as well the efficient pricing and reallocation of capital away from assets at risk to those that are more resilient. - Underpin the development and the growth of UK GF-related products and services. - Enable a vibrant ecosystem of UK enterprises providing CER analytics and realise the opportunity for UK plc of being a world-leader in the creation and provision of CER services. Our vision is that CERAF will be the nucleus of a new national centre established to deliver world-leading research, information, and innovation to systematically accelerate the adoption and use of CER data and analytics by FIs and to unlock opportunities for the UK to lead internationally in delivering CER services to support advancements in greening finance and financing green globally It aims to overcome the following barriers: 1) Making existing data on hazards, vulnerabilities, and exposures more accessible and useable for FIs, with clearly communicated confidence and with analytics that does not yet exist being secured; 2) Consistency and standards to reduce fragmentation, facilitate innovative products and enable the efficient flow and use of data; 3) Assurance and suitability are needed to understand which CER analytics are best suited for particular uses and provide transparency into underlying data and methodologies, so that CER analytics can be trusted and used; 4) Unlocking innovation through supporting FIs to test new approaches in a lower-risk way; and 5) Building capability, knowledge, and skills within FIs to analyse and interpret CER data. Resolving these barriers is a necessary condition for repricing capital and avoiding its misallocation, and achieving the UK's ambitions on GF.