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) to develop international research leaders in statistics and operational research (STOR) through a programme in which cutting-edge industrial challenge is the catalyst for methodological advance. Our proposal addresses the priority area 'Statistics for the 21st Century' through research training in cutting-edge modelling and inference for large, complex and novel data structures. It crucially recognises that many contemporary challenges in statistics, including those arising from industry, also engage with constraint, optimisation and decision. The proposal brings together LU's academic strength in STOR (>50FTE) with a distinguished array of highly committed industrial and international academic partners. Our shared vision is a CDT that produces graduates capable of the highest quality research with impact and equipped with an array of leadership and other skills needed for rapid career progression in academia or industry. The proposal builds on the strengths of an existing EPSRC-funded CDT that has helped change the culture in doctoral training in STOR through an unprecedented level of engagement with industry. The proposal takes the scale and scientific ambition of the Centre to a new level by: * Recruiting and training 70 students, across 5 cohorts, within a programme drawing on industrial challenge as the catalyst for research of the highest quality; * Ensuring all students undertake research in partnership with industry: 80% will work on doctoral projects jointly supervised and co-funded by industry; all others will undertake industrial research internships; * Promoting a culture of reproducible research under the mentorship and guidance of a dedicated Research Software Engineer (industry funded); * Developing cross-cohort research-clusters to support collaboration on ambitious challenges related to major research programmes; * Enabling students to participate in flagship research activities at LU and our international academic partners. The substantial growth in data-driven business and industrial decision-making in recent years has signalled a step change in the demand for doctoral-level STOR expertise and has opened the skills gap further. The current CDT has shown that a cohort-based, industrially engaged programme attracts a diverse range of the very ablest mathematically trained students. Without STOR-i, many of these students would not have considered doctoral study in STOR. We believe that the new CDT will continue to play a pivotal role in meeting the skills gap. Our training programme is designed to do more than solve a numbers problem. There is an issue of quality as much as there is one of quantity. Our goal is to develop research leaders who can innovate responsibly and secure impact for their work across academic, scientific and industrial boundaries; who can work alongside others with different skills-sets and communicate effectively. An integral component of this is our championing of ED&I. Our external partners are strongly motivated to join us in achieving these outcomes through STOR-i's cohort-based programme. We have little doubt that our graduates will be in great demand across a wide range of sectors, both industrial and academic. Industry will play a key role in the CDT. Our partners are helping to co-design the programme and will (i) co-fund and co-supervise doctoral projects, (ii) lead a programme of industrial problem-solving days and (iii) play a major role in leadership development and a range of bespoke training. The CDT benefits from the substantial support of 10 new partners (including Morgan Stanley, ONS Data Science Campus, Rolls Royce, Royal Mail, Tesco) and continued support from 5 existing partners (including ATASS, BT, NAG, Shell), with many others expected to contribute.

This CDT will train a cohort of 60 students to have the skills and experience that enables them to become leaders in Distributed Algorithms: capitalising on "Future Computing Systems" to move "Towards a Data-Driven Future". Commodity Data Science is already pervasive. This motivates today's pressing need for highly-trained data scientists. This CDT will empower tomorrow's leaders of data science. The UK (and world) needs data scientists that can best exploit tomorrow's computational resources to harvest the new 'oil': the information present in data. As our graduates' careers progress, many cored architectures will become increasingly commonplace. We anticipate millions more cores in tomorrow's desktops than today's. This core count will challenge the assumption made by current Big Data middleware (e.g., Spark and TensorFlow) that the details of future computing systems can be decoupled from the development of data science tools and techniques. More specifically, it will become imperative that data scientists understand how to design algorithms that can operate effectively in environments where data movement is the key performance bottleneck. To meet this need, we will provide training that ensures we generate highly-employable individuals who have both an understanding of the design of future computer hardware as well as an understanding of how and when to flex the algorithmic solutions to best exploit the computational resources that will exist in the future. From the outset, the students will be embedded in a computing environment that anticipates the hardware resources that will arrive on their desks after they graduate, not the hardware that exists today. The cohort of students provides the critical mass that motivates engagement with internationally-leading supercomputing centres: STFC's Hartree Centre is an integral part of the team; links we have established with IBM Research in the US will provide students with access to state-of-the-art computing hardware. This anticipation of future computing capability will ensure our graduates are highly employable, but also help motivate end-user organisations to engage with the CDT. We have identified such end-user organisations that span two themes: defence and security; manufacturing. Organisations in these themes are driven by performance demands and efficiency requirements respectively. We will align the training we provide with the needs of the cohort, the theme and the individual. Each studentship will have two academic supervisors (one aligned with the "Future Computing Systems" and one aligned with moving "Towards a Data-Driven Future") and at least one supervisor from a project partner. This supervisory team will co-define the scope of each studentship. Once the high quality student has been selected and recruited, we will work with the student to define the training that aligns with their needs and the specific demands of the studentship. Our training provision will include the training needs associated with both the "Future Computing Systems" and "Towards a Data-Driven Future" priority areas. We will use guest lectures from, for example, IBM (as used to train Fast Track civil servants) and UC Berkeley to ensure we maximise our graduates' ability to thrive and to become tomorrow's leaders in Distributed Algorithms.