The worldwide software market, estimated at $250 billion per annum, faces a disruptive challenge unprecedented since its inception: for performance and energy reasons, parallelism and heterogeneity now pervade every layer of the computing systems infrastructure, from the internals of commodity processors (manycore), through small scale systems (GPGPUs and other accelerators) and on to globally distributed systems (web, cloud). This pervasive parallelism renders the hierarchies, interfaces and methodologies of the sequential era unviable. Heterogeneous parallel hardware requires new methods of compilation for new programming languages supported by new system development strategies. Parallel systems, from nano to global, create difficult new challenges for modelling, simulation, testing and verification. This poses a set of urgent interconnected problems of enormous significance, impacting and disrupting all research and industrial sectors which rely upon computing technology. Our CDT will generate a stream of more than 50 experts, prepared to address these challenges by taking up key roles in academic and industrial research and development labs, working to shape the future of the industry. The research resources and industrial connections available to our CDT make us uniquely well placed within the UK to deliver on these aspirations. The "pervasive parallelism challenge" is to undertake the fundamental research and design required to transform methods and practice across all levels of the ICT infrastructure, in order to exploit these new technological opportunities. Doing so will allow us to raise the management of heterogeneous concurrency and parallelism from a niche activity in the care of experts, to a regularised component of the mainstream. This requires a steady flow of highly educated, highly skilled practitioners, with the ability to relate to opportunities at every level and to communicate effectively with specialists in related areas. These highly skilled graduates must not only have deep expertise in their own specialisms, but crucially, an awareness of relationships to the surrounding computational system. The need for fundamental work on heterogeneous parallelism is globally recognised by diverse interest groups. In the USA, reports undertaken by the Computing Community Consortium and the National Research Council recognise the paradigm shift needed for this technology to be incorporated into research and industry alike. Both these reports were used as fundamental arguments in initiating the call for proposals by the National Science Foundation (NSF) on Exploiting Parallelism and Scalability, in the context of the NSF's Advanced Computing Infrastructure: Vision and Strategic Plan which calls for fundamental research to answer the question of "how to enable the computational systems that will support emerging applications without the benefit of near-perfect performance scaling from hardware improvements." Similarly, the European Union has identified the need for new models of parallelism as part of its Digital Agenda. Under the agenda goals of Cloud Computing and Software and Services, parallelism plays a crucial role and the Commission asserts the need for a deeper understanding and new models of parallel computation that will enable future technology. Given the UK's global leadership status it is imperative that similar questions be posed and answered here.

The Programme is organised into two themes. Research theme one will develop new computer vision algorithms to enable efficient search and description of vast image and video datasets - for example of the entire video archive of the BBC. Our vision is that anything visual should be searchable for, in the manner of a Google search of the web: by specifying a query, and having results returned immediately, irrespective of the size of the data. Such enabling capabilities will have widespread application both for general image/video search - consider how Google's web search has opened up new areas - and also for designing customized solutions for searching. A second aspect of theme 1 is to automatically extract detailed descriptions of the visual content. The aim here is to achieve human like performance and beyond, for example in recognizing configurations of parts and spatial layout, counting and delineating objects, or recognizing human actions and inter-actions in videos, significantly superseding the current limitations of computer vision systems, and enabling new and far reaching applications. The new algorithms will learn automatically, building on recent breakthroughs in large scale discriminative and deep machine learning. They will be capable of weakly-supervised learning, for example from images and videos downloaded from the internet, and require very little human supervision. The second theme addresses transfer and translation. This also has two aspects. The first is to apply the new computer vision methodologies to `non-natural' sensors and devices, such as ultrasound imaging and X-ray, which have different characteristics (noise, dimension, invariances) to the standard RGB channels of data captured by `natural' cameras (iphones, TV cameras). The second aspect of this theme is to seek impact in a variety of other disciplines and industry which today greatly under-utilise the power of the latest computer vision ideas. We will target these disciplines to enable them to leapfrog the divide between what they use (or do not use) today which is dominated by manual review and highly interactive analysis frame-by-frame, to a new era where automated efficient sorting, detection and mensuration of very large datasets becomes the norm. In short, our goal is to ensure that the newly developed methods are used by academic researchers in other areas, and turned into products for societal and economic benefit. To this end open source software, datasets, and demonstrators will be disseminated on the project website. The ubiquity of digital imaging means that every UK citizen may potentially benefit from the Programme research in different ways. One example is an enhanced iplayer that can search for where particular characters appear in a programme, or intelligently fast forward to the next `hugging' sequence. A second is wider deployment of lower cost imaging solutions in healthcare delivery. A third, also motivated by healthcare, is through the employment of new machine learning methods for validating targets for drug discovery based on microscopy images

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.