The UK RDRF brings together a number of research strands funded under the DET, EPSRC and ESRC portfolios over the last decade to create a national facility to tackle the vexed question of regional competitiveness and rebalancing the UK economy. Following the Scottish referendum there have been renewed calls for greater devolution to regions and core cities. This facility will bring together the big economic data and construct the high resolution models needed to support policy makers at national, regional and local level. It will innovate by building together a model of the fixed stock of buildings, including housing, commercial, warehousing and manufacturing, with a network model of key infrastructure. This will allow analysis of which policy nudges might be expected to overcome the inertia present in the historic geography of the UK. It will allow a common framework of data and evidence ti be used by regional and local policy professionals wishing to evaluate policy options. The whole facility is built on the opportunity created by CDT funding to develop a cohort of evidence based policy professionals and analysts to support the needs of a more devolved form of planning. We aim to support the creation of a 'community of practice' based on access to big economic data and open source analysis and modelling tools. We will host workshops and networks to spread best practice and create some institutional glue amongst the people concerned. Finally, we will engage local communities in the debate and bring the same evidence and tools to the public at large through crowd science and in-the-wild research engagement.

The DART project aims to pioneer a ground-breaking capability to enhance the performance and energy efficiency of reconfigurable hardware accelerators for next-generation computing systems. This capability will be achieved by a novel foundation for a transformation engine based on heterogeneous graphs for design optimisation and diagnosis. While hardware designers are familiar with transformations by Boolean algebra, the proposed research promotes a design-by-transformation style by providing, for the first time, tools which facilitate experimentation with design transformations and their regulation by meta-programming. These tools will cover design space exploration based on machine learning, and end-to-end tool chains mapping designs captured in multiple source languages to heterogeneous reconfigurable devices targeting cloud computing, Internet-of-Things and supercomputing. The proposed approach will be evaluated through a variety of benchmarks involving hardware acceleration, and through codifying strategies for automating the search of neural architectures for hardware implementation with both high accuracy and high efficiency.

CENTRE VISION Our vision for the new CDT in Financial Computing and Analytics is to as a national 'beacon' linking PhD & Masters' students, industry and academia in financial computing and analytics. We and our Industry partners are also central to the forthcoming investments in Big Data from EPSRC and ESRC (e.g. Business Datasafe). Its principal objective is to educate the next generation of elite PhDs with unparalleled, cross-disciplinary expertise in applied computing, analytics and financial mathematics, as well as in-depth sector understanding, to meet an increasing demand for their skills within the Financial Service Industry, Government, Retail and other Service sectors. Our existing DTC in Financial Computing is unique (there is no other research & training activity like it in the world) and by placing our PhD students in financial institutions and regulators it has had a major impact on the UK financial sector, as indicated by the Financial Times article (School for QUANTS) and our Letters of Support. The CDT is a new partnership between UCL, LSE and ICL, all providing MRes courses and PhD supervision. NATIONAL IMPORTANCE & GROWING NEED FOR CROSS-DISCIPLINARY SKILLS London is the world's leading international financial centre and the UK financial services industry is the key sector for the UK economy, contributed £124bn to the UK economy, generating a trade surplus of £36bn in 2010 and employing 1 million people. London is also the location for our financial regulators and world-class Retailers. Our Financial and other Service industries are therefore crucial to the UK's, and especially London's, continuing social and economic prosperity. Although we receive over 600 enquiries/applications per annum, and growing, recent reports by McKinsey and Accenture highlight the major and growing skills shortage of (postgrad) IT/data scientists in the USA 22,000 and the UK 4,000. EPSRC PRIORITIES AND RESEARCH The proposed CDT is aligned to EPSRC priorities across a number of Themes, in particular: Data to Knowledge (an ICT Theme priority), Industrially Focussed Mathematical Modelling (Mathematical Sciences) and New Digital Ventures (Digital Economy). The crucially important IT research challenges in just one area, namely the application of software engineering, AI and verification/correctness to algorithms for automated trading, illustrates the enormous research opportunities. IMPACT The current DTC in Financial Computing is acknowledged by the Department of Business Innovation & Skills as having had a major impact on our financial industry partners and on our academic partners. This will continue with the new CDT, impacting Regulators, government, Retailers and analytics companies. * STUDENTS - In 2011 the Centre funded more female PhD students than males, and in 2012 the Centre started 40 new PhD students if we count DTC funded students, students funded by other sources, such as retail and analytics companies, and industry-based part-time students. * ACADEMIA - UCL, LSE and Imperial College have all appointed new faculty in applied financial computing and business analytics; and UCL and ICL have started new Masters programmes. * INDUSTRY - many of the Banks now have established formal PhD programmes, in part due to the current DTC, and proved lecturers to the partners for industry-oriented programmes. * REGULATORS AND GOVERNMENT- we have placed PhD students in the BoE/FSA/PRA/FCA and the Cabinet Office, and as discussed in the Case for Support, we have held individual meetings and workshops with the Regulators (BoE, PRA, FCA) and with new (Retailer) partners (Tesco, BUPA, Unilever) to discuss how we can support them. * SOCIETAL - we encourage and support our PhD students in launching their own start-up, and we provide Masters and Undergraduate students to London-based start-ups, especially in the area called New Finance (e.g. P2P lending, crowdfunding).

This Centre for Doctoral training in Industrially Focused Mathematical Modelling will train the next generation of applied mathematicians to fill critical roles in industry and academia. Complex industrial problems can often be addressed, understood, and mitigated by applying modern quantitative methods. To effectively and efficiently apply these techniques requires talented mathematicians with well-practised problem-solving skills. They need to have a very strong grasp of the mathematical approaches that might need to be brought to bear, have a breadth of understanding of how to convert complex practical problems into relevant abstract mathematical forms, have knowledge and skills to solve the resulting mathematical problems efficiently and accurately, and have a wide experience of how to communicate and interact in a multidisciplinary environment. This CDT has been designed by academics in close collaboration with industrialists from many different sectors. Our 35 current CDT industrial partners cover the sectors of: consumer products (Sharp), defence (Selex, Thales), communications (BT, Vodafone), energy (Amec, BP, Camlin, Culham, DuPont, GE Energy, Infineum, Schlumberger x2, VerdErg), filtration (Pall Corp), finance (HSBC, Lloyds TSB), food and beverage (Nestle, Mondelez), healthcare (e-therapeutics, Lein Applied Diagnostics, Oxford Instruments, Siemens, Solitonik), manufacturing (Elkem, Saint Gobain), retail (dunnhumby), and software (Amazon, cd-adapco, IBM, NAG, NVIDIA), along with two consultancy companies (PA Consulting, Tessella) and we are in active discussion with other companies to grow our partner base. Our partners have five key roles: (i) they help guide and steer the centre by participating in an Industrial Engagement Committee, (ii) they deliver a substantial elements of the training and provide a broad exposure for the cohorts, (iii) they provide current challenges for our students to tackle for their doctoral research, iv) they give a very wide experience and perspective of possible applications and sectors thereby making the students highly flexible and extremely attractive to employers, and v) they provide significant funding for the CDT activities. Each cohort will learn how to apply appropriate mathematical techniques to a wide range of industrial problems in a highly interactive environment. In year one, the students will be trained in mathematical skills spanning continuum and discrete modelling, and scientific computing, closely integrated with practical applications and problem solving. The experience of addressing industrial problems and understanding their context will be further enhanced by periods where our partners will deliver a broad range of relevant material. Students will undertake two industrially focused mini-projects, one from an academic perspective and the other immersed in a partner organisation. Each student will then embark on their doctoral research project which will allow them to hone their skills and techniques while tackling a practical industrial challenge. The resulting doctoral students will be highly sought after; by industry for their flexible and quantitative abilities that will help them gain a competitive edge, and by universities to allow cutting-edge mathematical research to be motivated by practical problems and be readily exploitable.

The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications. There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data. In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning. The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning. We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics. The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.