We will train a cohort of 65 PhD students to tackle the challenge of Data Creativity for the 21st century digital economy. In partnership with over 40 industry and academic partners, our students will establish the technologies and methods to enable producers and consumers to co-create smarter products in smarter ways and so establish trust in the use of personal data. Data is widely recognised by industry as being the 'fuel' that powers the economy. However, the highly personal nature of much data has raised concerns about privacy and ownership that threaten to undermine consumers' trust. Unlocking the economic potential of personal data while tackling societal concerns demands a new approach that balances the ability to innovate new products with building trust and ensuring compliance with a complex regulatory framework. This requires PhD students with a deep appreciation of the capabilities of emerging technology, the ability to innovate new products, but also an understanding of how this can be done in a responsible way. Our approach to this challenge is one of Data Creativity - enabling people to take control of their data and exercise greater agency by becoming creative consumers who actively co-create more trusted products. Driven by the needs of industry, public sector and third sector partners who have so far committed £1.6M of direct and £2.8M of in kind funding, we will explore multiple sectors including Fast Moving Consumer Goods and Food; Creative Industries; Health and Wellbeing; Personal Finance; and Smart Mobility and how it can unlock synergies between these. Our partners also represent interests in enabling technologies and the cross cutting concerns of privacy and security. Each student will work with industry, public, third sector or international partners to ensure that their research is grounded in real user needs, maximising its impact while also enhancing their future employability. External partners will be involved in PhD co-design, supervision, training, providing resources, hosting placements, setting industry-led challenge projects and steering. Addressing the challenges of Data Creativity demands a multi-disciplinary approach that combines expertise in technology development and human-centred methods with domain expertise across key sectors of the economy. Our students will be situated within Horizon, a leading centre for Digital Economy research and a vibrant environment that draws together a national research Hub, CDT and a network of over 100 industry, academic and international partners. We currently provide access to a network of >80 potential supervisors, ranging from leading Professors to talented early career researchers. This extends to academic partners at other Universities who will be involved in co-hosting and supervising our students, including the Centre for Computing and Social Responsibility at De Montfort University. We run an integrated four-year training programme that features: a bespoke core covering key topics in Future Products, Enabling Technologies, Innovation and Responsibility; optional advanced specialist modules; internship and international exchanges; industry-led challenge projects; training in research methods and professional skills; modules dedicated to the PhD proposal, planning and write up; and many opportunities for cross-cohort collaboration including our annual industry conference, retreat and summer schools. Our Impact Fund supports students in deepening the impact of their research. Horizon has EDI considerations embedded throughout, from consideration of equal opportunities in recruitment to ensuring that we deliver an inclusive environment which supports diversity of needs and backgrounds in the student experience.

Public opinion on complex scientific topics can have dramatic effects on industrial sectors (e.g. GM crops, fracking, global warming). In order to realise the industrial and societal benefits of Autonomous Systems, they must be trustworthy by design and default, judged both through objective processes of systematic assurance and certification, and via the more subjective lens of users, industry, and the public. To address this and deliver it across the Trustworthy Autonomous Systems (TAS) programme, the UK Research Hub for TAS (TAS-UK) assembles a team that is world renowned for research in understanding the socially embedded nature of technologies. TASK-UK will establish a collaborative platform for the UK to deliver world-leading best practices for the design, regulation and operation of 'socially beneficial' autonomous systems which are both trustworthy in principle, and trusted in practice by individuals, society and government. TAS-UK will work to bring together those within a broader landscape of TAS research, including the TAS nodes, to deliver the fundamental scientific principles that underpin TAS; it will provide a focal point for market and society-led research into TAS; and provide a visible and open door to engage a broad range of end-users, international collaborators and investors. TAS-UK will do this by delivering three key programmes to deliver the overall TAS programme, including the Research Programme, the Advocacy & Engagement Programme, and the Skills Programme. The core of the Research Programme is to amplify and shape TAS research and innovation in the UK, building on existing programmes and linking with the seven TAS nodes to deliver a coherent programme to ensure coverage of the fundamental research issues. The Advocacy & Engagement Programme will create a set of mechanisms for engagement and co-creation with the public, public sector actors, government, the third sector, and industry to help define best practices, assurance processes, and formulate policy. It will engage in cross-sector industry and partner connection and brokering across nodes. The Skills Programme will create a structured pipeline for future leaders in TAS research and innovation with new training programmes and openly available resources for broader upskilling and reskilling in TAS industry.

In project BIOBEADS we propose to develop, in combination, new manufacturing routes to new products. Manufacturing will be based on a low-energy process that can be readily scaled up, or down, and the products will be biodegradable microbeads, microscapsules and microsponges, which share the performance characteristics of existing plastic microsphere products, but which will leave no lasting environmental trace. Using bio-based materials such as cellulose (from plants) and chitin (from crab or prawn shells), we will use continuous manufacturing methods to generate microspheres, hollow capsules and porous particles to replace the plastic microbeads currently in use in many applications. Cellulose and chitin are biodegradable and also part of the diet of many marine organisms, meaning they have straightforward natural breakdown routes and will not accumulate in the environment. BIOBEADS will be produced using membrane emulsification techniques. The project builds on our joint expertise in membrane emulsification for continuous production of tunable droplet sizes, dissolution of cellulose and chitin in green solvents and in characterization of nanoscale and microscale structures to study all aspects of particle formation from precursors, through formation processes, to degradation routes. Yhe primary focus will be spheres and capsules, for use in cosmetics and personal care formulations, but, by understanding the processes and mechanisms of formation of these spheres, we aim to be able to tailor particle properties to suit larger scale applications from paint stripping, to fillers in biodegradable plastics. The BIOBEADS research team will work with industrial partners, including very large manufacturers of personal care products, to ensure that the research conducted can be taken up and used, so having a real, positive impact on the manufacturing of new, more sustainble products.

Bayesian inference is a process which allows us to extract information from data. The process uses prior knowledge articulated as statistical models for the data. We are focused on developing a transformational solution to Data Science problems that can be posed as such Bayesian inference tasks. An existing family of algorithms, called Markov chain Monte Carlo (MCMC) algorithms, offer a family of solutions that offer impressive accuracy but demand significant computational load. For a significant subset of the users of Data Science that we interact with, while the accuracy offered by MCMC is recognised as potentially transformational, the computational load is just too great for MCMC to be a practical alternative to existing approaches. These users include academics working in science (e.g., Physics, Chemistry, Biology and the social sciences) as well as government and industry (e.g., in the pharmaceutical, defence and manufacturing sectors). The problem is then how to make the accuracy offered by MCMC accessible at a fraction of the computational cost. The solution we propose is based on replacing MCMC with a more recently developed family of algorithms, Sequential Monte Carlo (SMC) samplers. While MCMC, at its heart, manipulates a single sampling process, SMC samplers are an inherently population-based algorithm that manipulates a population of samples. This makes SMC samplers well suited to the task of being implemented in a way that exploits parallel computational resources. It is therefore possible to use emerging hardware (e.g., Graphics Processor Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Intel's Xeon Phis as well as High Performance Computing (HPC) clusters) to make SMC samplers run faster. Indeed, our recent work (which has had to remove some algorithmic bottlenecks before making the progress we have achieved) has shown that SMC samplers can offer accuracy similar to MCMC but with implementations that are better suited to such emerging hardware. The benefits of using an SMC sampler in place of MCMC go beyond those made possible by simply posing a (tough) parallel computing challenge. The parameters of an MCMC algorithm necessarily differ from those related to a SMC sampler. These differences offer opportunities for SMC samplers to be developed in directions that are not possible with MCMC. For example, SMC samplers, in contrast to MCMC algorithms, can be configured to exploit a memory of their historic behaviour and can be designed to smoothly transition between problems. It seems likely that by exploiting such opportunities, we will generate SMC samplers that can outperform MCMC even more than is possible by using parallelised implementations alone. Our interactions with users, our experience of parallelising SMC samplers and the preliminary results we have obtained when comparing SMC samplers and MCMC make us excited about the potential that SMC samplers offer as a "New Approach for Data Science". Our current work has only begun to explore the potential offered by SMC samplers. We perceive significant benefit could result from a larger programme of work that helps us understand the extent to which users will benefit from replacing MCMC with SMC samplers. We propose a programme of work that combines a focus on users' problems with a systematic investigation into the opportunities offered by SMC samplers. Our strategy for achieving impact comprises multiple tactics. Specifically, we will: use identified users to act as "evangelists" in each of their domains; work with our hardware-oriented partners to produce high-performance reference implementations; engage with the developer team for Stan (the most widely-used generic MCMC implementation); work with the Industrial Mathematics Knowledge Transfer Network and the Alan Turing Institute to engage with both users and other algorithmic developers.

The Horizon institute is a multidisciplinary centre of excellence for Digital Economy (DE) research. The core mission of Horizon has been to balance the opportunities arising from the capture, analysis and use of personal data with an awareness and understanding of human and social values. The focus on personal data in a wide range of contexts has required the development of a broad set of multidisciplinary competencies allowing us to build links from foundational algorithms and system to issues of society and policy. We follow a user-centred approach, undertaking research in the wild based on principles of open innovation. Horizon now encompasses over 50 researchers, spanning Computing, Engineering, Law, Psychology, Social Sciences, Business and the Humanities. It has grown a diverse network of over 200 external partners who are involved in ongoing collaborative research and impact with Horizon, ranging from major international corporations to SMEs, from a wide variety of sectors, alongside government and civil society groups. We have also established a CDT in the third wave of funding that will eventually deliver 150 PhDs. Our critical mass of researchers, partners, students and funding has already led to over 800 peer-reviewed publications, composed of: 277 journal articles, 51 books and book chapters, and 424 conference papers, in a total of 15 different disciplines. Over the years Horizon's focus has evolved from an emphasis on the collection and understanding of personal data to consider the user-centred design and development of data-driven products. This proposal builds on our established interdisciplinary competencies to deliver research and impact to ensure that future data-driven products can be both co-created and trusted by consumers. Core to our current vision is the idea that future products will be hybrids of both the digital and the physical. Physical products are increasingly augmented with digital capabilities, from data footprints that capture their provenance to software that enables them to adapt their behaviour. Conversely, digital products are ultimately physically experienced by people in some real-world context and increasingly adapt to both. This real-world context is social; hence the data is social and often implicates groups, not just individuals. We foresee that this blending of physical and digital will drive the merging of traditional goods, services and experiences into new forms of product. We also foresee that - just as today's social media services are co-created by consumers who provide content and data - so will be these new data-driven products. At the same time, we are also witnessing a crisis of trust concerning the commercial use of personal data that threatens to undermine this vision of data-driven products. Hence, it is vitally important to build trust with consumers and operate within an increasingly complex regulatory environment from the earliest stages of innovating future products. Our user-centred approach involves external partners and the public in "research-in-the-wild", grounding our fundamental research in real world challenges. Our delivery programme combines a bottom-up approach in which researchers are given the opportunity (and provided with the skills) to follow new impact opportunities in collaboration with partners as they arise (our Agile programme), with a top-down approach that strategically coordinates how these activities are targeted at wider communities (our Campaigns programme, with successive focus on Consumables, Co-production and Welfare), and reflective processes that allow us to draw out broader conclusions for the widest possible impact (our Cross-Cutting programme). Throughout we aim to continue to develop the capacity in our researchers, the wider DE research community and more broadly within society, to engage in responsible innovation using personal data within the Digital Economy.