Adarga

The COMPASS Centre for Doctoral Training will provide high-calibre cohort-based training for over 55 PhD students in computational statistics and data science. The current disruptive data revolution has revealed new ways of using data to enhance productivity and improve citizens' well-being, and created responsible, effective and transformative ideas that were undreamt of only a few years ago. It is no surprise that the revolution has not only created new classes of data-centred companies, but also whole new data science groupings in many existing organizations. Big and complex data are now ubiquitous and fundamental for research and development, including in our integrated CDT academic partner disciplines of economics, education, engineering, medicine, computer, geographical, earth and life sciences. Similarly, for our external partners: businesses such as Adarga, CheckRisk, EDF, GSK, SCIEX, Shell and Trainline; and crucial government agencies such as the Atomic Weapons Establishment, GCHQ, Office for National Statistics and the UK Space Agency. Exploiting the full potential of big and complex data requires advanced statistical methods and computation working together, hence the need for computational statistics and data science. Bristol has long-established world-leading experience in computational statistics, a broad base of already engaged and co-creative statistical academic and dynamic external partners, excellent facilities, and extensive experience of running successful CDTs under the auspices of the Bristol Doctoral College. The societal, scientific and economic value of unlocking the potential in data has spurred demand for people trained to PhD level in computational statistics and data science: demand dramatically exceeds supply, internationally and in the UK. A COMPASS PhD will be highly valued for its focus on advanced technical, interdisciplinary and professional training, at a time where there are a large and increasing numbers of appealing employment opportunities. COMPASS will recruit the best students from numerate backgrounds and provide multimodal training within and across cohorts. This will include an assessed programme of taught coursework spanning a broad range of core and crossover statistical topics, reflecting strong historical and future links to our academic partner disciplines, such as causality in medical statistics, multilevel modelling in education or Bayesian modelling in genetics. Modern statistical practice typically involves interdisciplinary teams. Cohort and cross-cohort activities are essential for modern doctoral training and permeate the design of COMPASS. We will adopt tried and trusted cohort training methods, such as group work, group and partner projects, Masterclasses, and innovative cross-cohort activities such as COMPASS policy workshops, statistical consultancy teams and rapid response teams (small teams, formed at short notice, with staff from partners, to address important and urgent problems in their business, a co-creation idea from the Office for National Statistics). Our academic and external partners will be fully integrated in our training programme and its delivery and are committed to providing significant personnel and resources to support COMPASS throughout its eight-year life. Through alignment with the UK Academy of Postgraduate Training in Statistics, the University of Bristol's Jean Golding Institute for Data Intensive Research, the national Alan Turing Institute and the Heilbronn Institute for Mathematical Research Institute, COMPASS will provide a diverse, fulfilling and outstanding doctoral student experience. COMPASS will be an attractive focal point for the best students, preparing them for rewarding, impactful careers, and enabling them to make crucial contributions to the health, productivity, connectivity and resilience of the UK and its citizens.

Our mission is to train the next generations of innovators in responsible, data-driven and knowledge-intensive human-in-the-loop AI systems. Our innovative, cohort-based training programme will deliver cohorts of highly trained PhD graduates with the skills to design and implement complex interactive AI pipelines solving societally important problems in responsible ways. While fully autonomous artificial intelligence dominates today's headlines in the form of self-driving cars and human-level game play, the key AI challenges of tomorrow are posed by the need for interactive knowledge-intensive systems in which the human plays an essential role, be it as an end-user providing relevant case-specific knowledge or interrogating the system, an operator requiring crucial information to be presented in an intelligible form, a supervisor requiring confirmation that the system's performance remains within acceptable limits, or a regulator assessing to what extent the system operates according to exacting standards concerning transparency, accountability and fairness. Each of these examples demonstrates a need for specific and meaningful interaction between the AI system and human(s). The examples also demonstrate the importance of knowledge for achieving human-level interaction, in addition to the data driving the machine learning aspect of the system. In close conversation with our industry partners we thus identified Interactive Artificial Intelligence (IAI) as a core sub-discipline of AI where the need for and deficit in advanced AI skills is abundantly evident while being homogeneous enough to have intellectual integrity and be taught and researched within the context of a single CDT. The most important aspects of the training programme are: - Knowledge-Driven AI and Data-Driven AI are core components treated in a close symbiotic relationship: the former uses knowledge in processes such as reasoning, argumentation and dialogue, but in such a way that data is treated as a first-class citizen; the latter starts from data but emphasises knowledge-intensive forms of machine learning such as relational learning which take knowledge as an additional input. - Human-AI Interaction is another core component addressing all human-in-the-loop aspects, overseen by a co-investigator from the human-computer interaction field. - Responsible AI is underpinning not just the taught first year but the students' doctoral training throughout all four years, overseen by two dedicated co-investigators with backgrounds in IT law and industrial codes of practice. Other skill requirements from stakeholders include: the ability to design and implement complete end-to-end systems; acquiring depth in some AI-related subjects without sacrificing breadth; the ability to work in teams of people with diverse skill sets; and being able to take on a role as "AI ambassadors" who are able to inspire but also to manage expectations through their in-depth understanding of the strengths and weaknesses of different AI techniques. The IAI training programme is designed to achieve this by strongly emphasising cohort-based training. Students will develop their projects and coursework within an innovative software environment which means easy integration of their work with that of others. This virtual hub is complemented by a physical hub where all cohorts are colocated -- together both hubs will strongly promote interaction both within and between cohorts: e.g., projects can aim at improving or extending software produced by the previous cohort, so that senior students can be involved in mentoring their juniors. In summary, the IAI training programme pulls together Bristol's unique and comprehensive strengths in doctoral training and AI to deliver highly trained AI innovators, equipping them with essential skills to deliver the interactive AI technology society requires to deal with current and future challenges.