Within the next few years the number of devices connected to each other and the Internet will outnumber humans by almost 5:1. These connected devices will underpin everything from healthcare to transport to energy and manufacturing. At the same time, this growth is not just in the number or variety of devices, but also in the ways they communicate and share information with each other, building hyper-connected cyber-physical infrastructures that span most aspects of people's lives. For the UK to maximise the socio-economic benefits from this revolutionary change we need to address the myriad trust, identity, privacy and security issues raised by such large, interconnected infrastructures. Solutions to many of these issues have previously only been developed and tested on systems orders of magnitude less complex in the hope they would 'scale up'. However, the rapid development and implementation of hyper-connected infrastructures means that we need to address these challenges at scale since the issues and the complexity only become apparent when all the different elements are in place. There is already a shortage of highly skilled people to tackle these challenges in today's systems with latest estimates noting a shortfall of 1.8M by 2022. With an estimated 80Bn malicious scans and 780K records lost daily due to security and privacy breaches, there is an urgent need for future leaders capable of developing innovative solutions that will keep society one step ahead of malicious actors intent on compromising security, privacy and identity and hence eroding trust in infrastructures. The Centre for Doctoral Training (CDT) 'Trust, Identity, Privacy and Security - at scale' (TIPS-at-Scale) will tackle this by training a new generation of interdisciplinary research leaders. We will do this by educating PhD students in both the technical skills needed to study and analyse TIPS-at-scale, while simultaneously studying how to understand the challenges as fundamentally human too. The training involves close involvement with industry and practitioners who have played a key role in co-creating the programme and, uniquely, responsible innovation. The implementation of the training is novel due to its 'at scale' focus on TIPS that contextualises students' learning using relevant real-world, global problems revealed through project work, external speakers, industry/international internships/placements and masterclasses. The CDT will enrol ten students per year for a 4-year programme. The first year will involve a series of taught modules on the technical and human aspects of TIPS-at-scale. There will also be an introductory Induction Residential Week, and regular masterclasses by leading academics and industry figures, including delivery at industrial facilities. The students will also undertake placements in industry and research groups to gain hands-on understanding of TIPS-at-scale research problems. They will then continue working with stakeholders in industry, academia and government to develop a research proposal for their final three years, as well as undertake internships each year in industry and international research centres. Their interdisciplinary knowledge will continue to expand through masterclasses and they will develop a deep appreciation of real-world TIPS-at-scale issues through experimentation on state-of-the-art testbed facilities and labs at the universities of Bristol and Bath, industry and a city-wide testbed: Bristol-is-Open. Students will also work with innovation centres in Bath and Bristol to develop novel, interdisciplinary solutions to challenging TIPS-at-scale problems as part of Responsible Innovation Challenges. These and other mechanisms will ensure that TIPS-at-Scale graduates will lead the way in tackling the trust, identity, privacy and security challenges in future large, massively connected infrastructures and will do so in a way that considers wider sosocial responsibility.

There are many interesting open questions at the interface between applied mathematics, scientific computing and applied statistics. Mathematics is the language of science, we use it to describe the laws of motion that govern natural and technological systems. We use statistics to make sense of data. We develop and test computer algorithms that make these ideas concrete. By bringing these concepts together in a systematic way we can validate and sharpen our hypothesis about the underlying science, and make predictions about future behaviour. This general field of Uncertainty Quantification is a very active area of research, with many challenges; from intellectual questions about how to define and measure uncertainty to very practical issues concerning the need to perform intensive computational experiments as efficiently as possible. ICONIC brings together a team of high profile researchers with the appropriate combination of skills in modeling, numerical analysis, statistics and high performance computing. To give a concrete target for impact, the ICONIC project will focus initially on Uncertainty Quantification for mathematical models relating to crime, security and resilience in urban environments. Then, acknowledging that urban analytics is a very fast-moving field where new technologies and data sources emerge rapidly, and exploiting the flexibility built into an EPSRC programme grant, we will apply the new tools to related city topics concerning human mobility, transport and infrastructure. In this way, the project will enhance the UK's research capabilities in the fast-moving and globally significant Future Cities field. The project will exploit the team's strong existing contacts with Future Cities laboratories around the world, and with nonacademic stakeholders who are keen to exploit the outcomes of the research. As new technologies emerge, and as more people around the world choose to live and work in urban environments, the Future Cities field is generating vast quantities of potentially valuable data. ICONIC will build on the UK's strength in basic mathematical sciences--the cleverness needed to add value to these data sources--in order to produce new algorithms and computational tools. The research will be conducted alongside stakeholders--including law enforcement agencies, technical IT and infrastructure providers, utility companies and policy-makers. These external partners will provide feedback and challenges, and will be ready to extract value from the tools that we develop. We also have an international Advisory Board of committed partners with relevant expertise in academic research, policymaking, law enforcement, business engagement and public outreach. With these structures in place, the research will have a direct impact on the UK economy, as the nation competes for business in the global Future Cities marketplace. Further, by focusing on crime, security and resilience we will directly improve the lives of individual citizens.

This project aims to transform awareness and understanding of the "Incel' community (Involuntary Celibates), and the contexts in which susceptible young men are indoctrinated into misogynistic extremism and in some cases, mass murder. Creative research methods are core to the approach due to the research team's experience of using these methods with disenfranchised communities (e.g. Incels and neurodivergent groups) as well as the practices of Incels, which employ digital art-making within the processes of indoctrination. Working in collaboration with both counter-terrorism command and Prevent programming with the Metropolitan Police (MET) and Police Scotland, the project builds on the research team's previous experiences, using interdisciplinary and creative methods to develop training for professionals working in the contexts of education, health, social care and the criminal justice system. Through the development of an expert interdisciplinary network, novel methods, and a socially engaged approach, the project's preventative orientation seeks to save the lives of potential victims as well as perpetrators. This will be achieved through the development of new knowledge about the culture of Incels, the identities and experiences of this complex community and the factors contributing to the risk of extreme violence and hate crimes. In partnership with the Met and Police Scotland we will develop (i) training and resources to be used in identifying and working with Incel members; (ii) establish and consult with an expert interdisciplinary network towards a preventative programme; (iii) communicate new knowledge to enhance public awareness and understanding through creative media and publications.

A key factor in reducing potential gun crime is to detect someone carrying a gun before they can commit a criminal act. This detection can be achieved by the existing, and widespread, CCTV camera network in the UK. However, the performance of operators in interpreting CCTV imagery is variable as they are trying to detect essentially a very rare threat event. Additionally, current automated systems for detecting possible anomalous behaviour have been found to have varying success. We propose the development of a new machine learning system for the detection of individuals carrying guns which will combine both human and machine-based factors. Using selected CCTV footage which depicts people carrying concealed guns, and other control individuals, the proposal will establish what overt and covert cues (essentially conscious and subconscious cues) experienced CCTV operators actually attend to when identifying potential gun-carrying individuals from such CCTV imagery. In parallel, a machine learning approach will establish the machine recognised cues for such individuals. The separate human and machine cues will then be combined to form a new machine learning approach which will be fully tested. The system will be capable of learning and reacting to local gun crime factors which will aid its usefulness and deployment capability.

The COVID-19 crisis is changing the shape of crime. Drawing on crime science, this research will inform evidence-based policy and practice. Lockdown requires people to stay home, leading to domestic violence and child abuse increases. Yet social distancing means police are arresting fewer suspects: reduced services at time of greater need. COVID-19 gives fraudsters a 'conversation starter' to approach people in-person, via text, email and online. Remote working and online leisure activities, furloughs and financial difficulties, provide more potential targets for online crimes of various types. Vulnerable groups including the elderly and disabled are more at risk. Yet a Harvard study (Kissler et al. Science, 14 April) suggests that, absent a vaccine, social distancing may continue into 2022, perhaps 2024. So we will anticipate crime effects of prolonged, graduated or cyclical exit strategies. We will also anticipate post-crisis scenarios, seeking to sustain declines in crimes like burglary, to avoid them returning to 'normal'. We will use (1) national police data, (2) detailed data from three police partners, (3) fraud and e-crime data from industry, and (4) sources from other agencies such as Childline (for unreported crime). Pre/post-change analysis will use a combination of time-series and spatial modelling. Nesting force-level analysis in the national and international context will allow us to gauge scalability. We have police and industry partners, national (Home office, National Police Chief's Council, College of Policing) and international advisors. The aim is to inform policy and practice, producing 16 deliverables including policy and practice briefings and research articles.