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.

A growing consensus identifies autonomous systems as core to future UK prosperity, but only if the present skills shortage is addressed. The AIMS CDT was founded in 2014 to address the training of future leaders in autonomous systems, and has established a strong track record in attracting excellent applicants, building cohorts of research students and taking Oxford's world-leading research on autonomy to achieve industrial impact. We seek the renewal of the CDT to cement its successes in sustainable urban development (including transport and finance), and to extend to applications in extreme and challenging environments and smart health, while strengthening training on the ethical and societal impacts of autonomy. Need for Training: Autonomous systems have been the subject of a recent report from the Royal Society, and an independent review from Professor Dame Wendy Hall and Jérôme Pesenti. Both reports emphatically underline the economic importance of AI to the UK, estimating that "AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035". Both reports also highlight the urgency of training many more skilled experts in autonomy: the summary of the Royal Society's report states "further support is needed to build advanced skills in machine learning. There is already high demand for people with advanced skills, and additional resources to increase this talent pool are critically needed." In contrast with pure Artificial Intelligence CDTs, AIMS places emphasis on the challenges of building end-to-end autonomous systems: such systems require not just Machine Learning, but the disciplines of Robotics and Vision, Cyber-Physical Systems, Control and Verification. Through this cross-disciplinary training, the AIMS CDT is in a unique position to provide positive economic and societal impacts for autonomous systems by 1) growing its existing strengths in sustainable urban development, including autonomous vehicles and quantitative finance, and 2) expanding its scope to the two new application pillars of extreme and challenging environments and smart health. AIMS itself provides evidence for the strong and increasing demand for training in these areas, with an increase in application numbers from 49 to 190 over the last five years. The increase in applications is mirrored by the increase in interest from industrial partners, which has more than doubled since 2014. Our partners span all application areas of AIMS and their contributions, which include training, internships and co-supervision opportunities, will immerse our students in a variety of research challenges linked with real-world problems. Training programme: AIMS has and will provide broad cohort training in autonomous intelligent systems; theoretical foundations, systems research, industry-initiated projects and transferable skills. It covers a comprehensive range of topics centered around a hub of courses in Machine Learning; subsequent spokes provide training in Robotics and Vision, Control and Verification, and Cyber-Physical Systems. The cohort-focused training program will equip our students with both core technical skills via weekly courses, research skills via mini and long projects, as well as transferable skills, opportunities for public engagement, and training on entrepreneurship and IP. The growing societal impacts of autonomous systems demand that future AIMS students receive explicit training in responsible and ethical research and innovation, which will be provided by ORBIT. Additionally, courses on AI ethics, safety, governance and economic impacts will be delivered by Oxford's world-leading Future of Humanity Institute, Oxford Uehiro Centre for Practical Ethics and Oxford Martin Programme on Technology and Employment.

Machine learning (ML) systems are increasingly being deployed across society, in ways that affect many lives. We must ensure that there are good reasons for us to trust their use. That is, as Baroness Onora O'Neill has said, we should aim for reliable measures of trustworthiness. Three key measures are: Fairness - measuring and mitigating undesirable bias against individuals or subgroups; Transparency/interpretability/explainability - improving our understanding of how ML systems work in real-world applications; and Robustness - aiming for reliably good performance even when a system encounters different settings from those in which it was trained. This fellowship will advance work on key technical underpinnings of fairness, transparency and robustness of ML systems, and develop timely key applications which work at scale in real world health and criminal justice settings, focusing on interpretability and robustness of medical imaging diagnosis systems, and criminal recidivism prediction. The project will connect with industry, social scientists, ethicists, lawyers, policy makers, stakeholders and the broader public, aiming for two-way engagement - to listen carefully to needs and concerns in order to build the right tools, and in turn to inform policy, users and the public in order to maximise beneficial impacts for society. This work is of key national importance for the core UK strategy of being a world leader in safe and ethical AI. As the Prime Minister said in his first speech to the UN, "Can these algorithms be trusted with our lives and our hopes?" If we get this right, we will help ensure fair, transparent benefits across society while protecting citizens from harm, and avoid the potential for a public backlash against AI developments. Without trustworthiness, people will have reason to be afraid of new ML technologies, presenting a barrier to responsible innovation. Trustworthiness removes frictions preventing people from embracing new systems, with great potential to spur economic growth and prosperity in the UK, while delivering equitable benefits for society. Trustworthy ML is a key component of Responsible AI - just announced as one of four key themes of the new Global Partnership on AI. Further, this work is needed urgently - ML systems are already being deployed in ways which impact many lives. In particular, healthcare and criminal justice are crucial areas with timely potential to benefit from new technology to improve outcomes, consistency and efficiency, yet there are important ethical concerns which this work will address. The current Covid-19 pandemic, and the Black Lives Matter movement, indicate the urgency of these pressing issues.

The UKRI Centre for Doctoral Training in "Application of Artificial Intelligence to the study of Environmental Risks" will develop a new generation of innovation leaders to tackle the challenges faced by societies across the globe living in the face of environmental risk, by developing new methods that exploit the potential of Artificial Intelligence (AI) approaches to the proper analysis of complex and diverse environmental data. It is made of multiple departments within Cambridge University, alongside the British Antarctic Survey and a wide range of partners in industry and policy. AI offers huge potential to transform our ability to understand, monitor and predict environmental risks, providing direct societal benefit as well as potential commercial opportunities. Delivering the UN 2030 Sustainable Development Agenda and COP 21 Paris Agreement present enormous and urgent challenges. Population and economic growth drive increased demands on a planet with finite resources; the planet's biodiversity is suffering increasing pressures. Simultaneously, humanity's vulnerabilities to geohazards are increasing, due to fragilities inherent in urbanisation in the face of risks such as floods, earthquake, and volcanic eruptions. Reliance on sophisticated technical infrastructures is a further exposure. Understanding, monitoring and predicting environmental risks is crucial to addressing these challenges. The CDT will provide the global knowledge leadership needed, by building partnership with leaders in industry, commerce, policy and academia in visionary, creative and cross-disciplinary teaching and research. Vast and growing datasets are now available that document our changing environment and associated risks. The application of AI techniques to these datasets has the potential to revolutionise our ability to build resilience to environmental hazards and manage environmental change. Harnessing the power of AI in this regard will support two of the four Grand Challenges identified in the UK's Industrial Strategy, namely, to put the UK at the forefront of the AI and data revolution and to maximise the advantages for UK industry from the global shift to clean growth. The students in the CDT will be trained in a broad range of aspects of the application of AI to environmental risk in a multi- disciplinary and enthusing research setting, to become world-leaders in the arena. They will undertake media training activities, public engagement, and training in the delivery of policy advice as well as the development of entrepreneurial skills and an understanding of the approach of business to sustainability. Discussion of the broader societal, legal and ethical dimensions will be integral to this training. In this way the CDT will seed a new domain of AI application in the UK that will become a champion for the subject globally.

PhD projects will be organised in three central themes that represent the core of our programme. The themes are aligned to the strategic priorities of our NHS partners and the overall vision of the CDT: A. AI-enabled diagnostics or prognostics [lead; McKendry]. Deep learning - the subset of machine learning that is based on a network structure loosely inspired by the human brain - enables networks to learn features from clinical data automatically. This gives them the ability to model complex non-linear relationships and such AI methods have found application in clinical diagnosis using either parameters typically embedded in an electronic health record (like blood test results) or the images produced during radiographic exams or in digital pathology suites. This theme will help us create, initiate and deploy academic research projects centred on clinical use cases of direct applicability in the hospitals where our Centre is based. Example projects might include the detection of radiology abnormality; characterisation of tissues and tissue abnormality (e.g. cancer staging); or the serial monitoring of disease. B. AI-enabled operations [lead; Marshall] The proximity of our Centre to the end-users of health technology prompts a second focus, on the use of AI methods to optimise care processes and pathways. We will ensure that our projects are academically focused, but will seek to create new approaches to investigate and characterise the performance of hospitals systems and processes - such as the flow of patients through emergency departments, AI-enabled projects that might shorten time-to-treatment or cancer waits. This will be the most translationally focused theme, seeking to surface and address key use cases of the greatest academic interest. C. AI-enabled therapeutics [lead; Denaxas]. Our final theme is forward looking; the use of deep learning and other AI methods in therapeutic inference or even in a therapy itself. AI methods may be most applicable here in mental health, where deployment of 'talking therapies' is as efficacious through the internet or telephony as face-to-face; or in the development of 'avatar therapies' such as that recently proposed at UCL for hallucinations. But a wide variety of research projects are conceivable, including rehabilitation following stroke; or indeed the use of AI monitoring of radiological change as a proxy endpoint for drug trials. This theme will help us focus cutting-edge work in our Centre around such use cases and novel methodology. The UK leads in the development of artificial intelligence technologies, investing around $850M between 2012-16, the third highest of any country. This has catalysed significant UK involvement of major global technology companies such as Alphabet and Apple, the creation of new UK-based AI companies such as Benevolent AI and DeepMind (both partners in our Centre) and the emergence of a vibrant UK SME community. 80% of AI companies on the UK Top 50 list are based in London, most with 30 minutes travel from UCL. Many of the most successful AI companies now focus on the application of AI in health, but the successful application of AI technologies such as deep learning has three key unmet needs; the identification of clinically relevant use cases, the availability of large quantities of high quality labelled data from NHS patients, and the availability of scientists and software engineers with the requisite algorithmic and programming skills. All three are addressed by our CDT, its novel NHS-embedded approach to training, linked to primary and social care and with close involvement of commercial partners, structured internships and leadership and entrepreneurship. This will create an entirely new cadre of individuals with both clinical knowledge and algorithmic/programming expertise, but also catalyse the creation and discovery of new large labelled datasets and exceptional clinical use cases informed by real-world clinical care.