The aim of this network is to establish the UK as the world leading authority in the joint area of Computational Statistics and Machine Learning (CompStat & ML) by advancing communication, interchange and collaboration within the UK between the disciplines of Computational Statistics (CompStat) and Machine Learning (ML). The UK has tremendous research strength and depth that is widely acknowledged as world leading in both the individual areas of Computational Statistics and Machine Learning. Despite each of these fields of research developing, largely, independently and having their own separate journals, international societies, conferences and curricula both areas of investigation share a common theoretical foundation based on the underlying formal principles of mathematical statistics and statistical inference. As such there is a natural diffusion of concepts, research and individuals between both disciplines. This network will seek to formalise as well as enhance this interchange and in the process capitalise on important synergies that will emerge from the combined and shared research agendas of CompStat & ML.

The aim of this network is to establish the UK as the world leading authority in the joint area of Computational Statistics and Machine Learning (CompStat & ML) by advancing communication, interchange and collaboration within the UK between the disciplines of Computational Statistics (CompStat) and Machine Learning (ML). The UK has tremendous research strength and depth that is widely acknowledged as world leading in both the individual areas of Computational Statistics and Machine Learning. Despite each of these fields of research developing, largely, independently and having their own separate journals, international societies, conferences and curricula both areas of investigation share a common theoretical foundation based on the underlying formal principles of mathematical statistics and statistical inference. As such there is a natural diffusion of concepts, research and individuals between both disciplines. This network will seek to formalise as well as enhance this interchange and in the process capitalise on important synergies that will emerge from the combined and shared research agendas of CompStat & ML.

Lancaster University (LU) proposes a Centre for Doctoral Training (CDT) whose goal is the development of international research leaders in statistics and operational research (STOR) through a programme in which industrial challenge is the catalyst for methodological advance. The proposal brings together LU's considerable academic strength in STOR with a formidable array of external partners, both academic and industrial. All are committed to the development of graduates capable of either leadership roles in industry or of taking their experience of and commitment to industrial engagement into academic leadership in STOR. The proposal develops an existing EPSRC-funded CDT (STOR-i) by a significant evolution of its mission which takes its degree of industrial engagement to a new level. This considerably enhanced engagement will further strengthen STOR-i's cohort-based training and will result in a minimum of 80% of students undertaking doctoral projects joint with industry, up from 50% in the current Centre. Industrial internships will be provided for those not following a PhD with industry. Industry will (i) play a role in steering the Centre, (ii) has co-designed the training programme, (iii) will co-fund and co-supervise industrial doctoral projects, (iv) will lead a programme of industrial problem-solving days and (v) will play a major role in the Centre's programme of leadership development. Industry's financial backing is providing for stipend enhancement and a range of infrastructure and training support as well as helping to bring STOR-i benefits to a wide audience. The total pledged support for STOR-i is over £5M (including £1.1M cash). The proposal addresses the priority area 'Industrially-Focussed Mathematical Modelling'. Within this theme we specifically target 'Statistics' (itself a priority area) and Operational Research (OR). This choice is motivated first by the pervasive need for STOR solutions within modern industrial problems and second by the widely acknowledged and long standing skills-shortage at doctoral level in these areas. Our partners' statements of support attest that the substantial recent growth in data acquisition and data-driven business and industrial decision-making have signalled a step change in the demand for high level STOR expertise and have opened the skills gap still wider. The current Centre has demonstrated that a high quality, industrially engaged programme of research training can create a high demand for places among the very ablest mathematically trained students, including many who would otherwise not have considered doctoral study in STOR. We believe that the new Centre will play a yet more strategic role than its predecessor in meeting the persistent skills gap. Our training programme is designed to do more than solve a numbers problem. There is an issue of quality of graduating doctoral students in STOR as much as there is one of quantity. Our goal is to develop research leaders who are able to secure impact for their work across academic, scientific and industrial boundaries; who can work alongside others who are differently skilled and who can communicate widely. Our external partners are strongly motivated to join us in achieving this through STOR-i's cohort-based training programme. We have little doubt that our graduates will be in great demand across a wide range of sectors, both industral and academic. The need for a Centre to deliver the training resides primarily in its guarantee of a critical mass of outstanding students. This firstly enables us to design a training programme around student cohorts in which peer to peer learning is a major feature. Second, we are able to attract and integrate the high quality contributions (both internal and external to LU) we need to create a programme of quality, scope and ambition.

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).