Climate change has been referred to by leading economists as the greatest market failure in human history, with disastrous impacts on human well-being and economic wealth. After years of neglect, the finance sector, at the heart of the economy, has woken up to this challenge, realizing that not integrating considerations of climate change into financial decision-making produces significant risks for financial investments and the stability of financial markets. Now there is a growing consensus, especially amongst financial leaders, that climate change is a material risk and that it needs to be effectively identified, measured, monitored and integrated into financial decision-making. A large ecosystem of organizations is implicated in effecting the necessary changes: (1) investors need to change the way they assess, monitor and respond to risks, (2) companies need to report new climate-related financial information, (3) data providers and consultancies need to develop new tools for forward-looking climate scenario analysis, (4) regulators need to change their regulatory and supervisory practices, and (5) NGOs are changing their advocacy practices to support these developments. Given the high interconnectedness of the financial system, the major challenge of these organizations is to change what they do in a way that cannot be coordinated on this scale and that is complicated by the inherent complexity and uncertainty of climate change. Against this background, this project investigates how organizations can tackle large-scale problems when they are caught in a complex web of interactions with other organizations. It examines in detail the practical challenges that emerge in the interactions with other organizations and how the local experiments in multiple organizations interact and contribute to emerging collective approaches to the large-scale problem (i.e., to climate risk). The project draws on a longitudinal, qualitative research approach in which a team of four researchers uses ethnographic methods to document in real-time the actions of multiple organizations to address climate risks. Over the course of four years, the team will collect detailed data on and analyse the actions of (1) financial investors (e.g., pension funds, insurance companies, asset managers etc.), (2) financial data providers and consultancies, (3) investor networks, and (4) NGOs. Data collection will unfold in three waves of one year each. The first wave will focus on an initial set of four leading organizations in the area of climate risks. In the two subsequent waves, the data collection will gradually be extended to cover a total of approx. 12 organizations. The focus will be on organizations headquartered or located in the UK with some limited data collection in other European countries. Ethnographic methods for data collection accomplish deep immersion in the work of others through participant observation (e.g., observing meetings, social interactions and work at the desk), open-ended interviews and the collection of documents. Theoretically, this research contributes to developing new theory in the area of management studies on how organizations can tackle large-scale, complex, social problems, known as 'grand challenges.' Methodologically, it develops new social science research methods that are better suited to capture the increasingly mobile and complex issues facing organizations today. Practically, it seeks to inform the actions of (1) policy-makers and regulators to make financial markets resilient and sustainable; (2) financial investors to minimize risks and safeguard returns; (3) consultancies and data providers to develop appropriate tools and data; and (4) NGOs to develop effective advocacy.

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.

Civil infrastructure is the key to unlocking net zero. To achieve the ambitious UK targets of net zero by 2050, we require innovative approaches to design, construction, and operation that prioritise energy efficiency, renewable resources, and low-carbon materials. Meeting net zero carbon emissions will require not only significant investment and planning, but also a radical shift in how we approach the design and management of our civil infrastructure. Reliable low carbon infrastructure sector solutions that meet real user needs are essential to ensure a smooth and safe transition to a net zero future. To address these challenges, the UK must develop highly skilled infrastructure professionals who can champion this urgent, complex, interconnected and cross-disciplinary transition to net zero infrastructure. This EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT) aims to lead this transformation by co-developing and co-delivering an inspirational doctoral training programme with industry partners. FIBE3 will focus on meeting the user needs of the construction and infrastructure sector in its pursuit of net zero. Our goal is to equip emerging talents from diverse academic and social backgrounds with the skills, knowledge and qualities to engineer the infrastructure needed to unlock net zero, including technological, environmental, economic, social and demographic challenges. Achievable outcomes will include a dynamic roadmap for the infrastructure that unlocks net zero, cohort-based doctoral student training with immersive industry experience, a CDT which is firmly embedded within existing net zero research initiatives, and expanded networks and outward-facing education. These outcomes will be centred around four thematic enablers: (1) existing and disruptive/new technologies, (2) radical circularity and whole life approach, (3) AI-driven digitalisation and data, and (4) risk-based systems thinking and connectivity. FIBE3 doctoral students will be trained to unlock net zero by evolving the MRes year to include intimate industry engagement through the novel introduction of a fourth dimension to our successful 'T-shaped' training model and designing the PhD with regular outward-facing deliverables. We have leveraged industry-borne ideas to align theory and practice, streamline business and research needs, and provide both academic-led and industry-led training activities. Cohort-based training in technical, commercial, transferable and personal skills will be provided for our graduates to become skilled professionals and leaders in delivering net zero infrastructure. FIBE3's alignment with real industry needs is backed by a 31 strong consortium, including owners, consultants, contractors, technology providers and knowledge transfer partners, who actively seek engagement for solutions and will support the CDT with substantial cash (£2.56M) and in-kind (£8.88M) contributions. At Cambridge, the FIBE3 CDT will be embedded within an inspirational research and training environment, a culture of academic excellence and within a department with strategic cross-cutting research themes that have net zero ambitions at their core. This is exemplified by Cambridge's portfolio of over £60M current aligned research grant funding and our internationally renowned centres and initiatives including the Digital Roads of the Future Initiative, the Centre for Smart Infrastructure and Construction, Cambridge Zero and Cambridge Centres for Climate Repair and Carbon Credits, as well as our strong partnerships with UK universities and leading academic centres across the globe. Our proposed vision, training structure and deliverables are exciting and challenging; we are confident that we have the right team to deliver a highly successful FIBE3 CDT and to continue to develop outstanding PhD graduates who will be net zero infrastructure champions of the future.