EPSRC Centre for Doctoral Training in Computational Statistics and Data Science: COMPASS

Lead Research Organisation: University of Bristol
Department Name: Mathematics


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.

Planned Impact

The COMPASS Centre for Doctoral Training will have the following impact.

Doctoral Students Impact.

I1. Recruit and train over 55 students and provide them with a broad and comprehensive education in contemporary Computational Statistics & Data Science, leading to the award of a PhD. The training environment will be built around a set of multilevel cohorts: a variety of group sizes, within and across year cohort activities, within and across disciplinary boundaries with internal and external partners, where statistics and computation are the common focus, but remaining sensitive to disciplinary needs. Our novel doctoral training environment will powerfully impact on students, opening their eyes to not only a range of modern technical benefits and opportunities, but on the power of team-working with people from a range of backgrounds to solve the most important problems of the day. They will learn to apply their skills to achieve impact by collaborative working with internal and external partners, such as via our Rapid Response Teams, Policy Workshops & Statistical Clinics.

I2. As well as advanced training in computational statistics and data science, our students will be impacted by exposure to, and training in, important cognate topics such as ethics, responsible innovation, equality, diversity and inclusion, policy, effective communication and dissemination, enterprise, impact and consultancy skills. It is vital for our students to understand that their training will enable them to have a powerful impact on the wider world, so, e.g., AI algorithms they develop should not be discriminatory, and statistical methodologies should be reproducible, and statistical results accurately and comprehensibly communicated to the general public and policymakers.

I3. The students will gain experience via collaborations with academic partners within the University in cognate disciplines, and a wide range of external industrial & government partners. The students will be impacted by the structured training programmes of the UK Academy of Postgraduate Training in Statistics, the Bristol Doctoral College, the Jean Golding Institute, the Alan Turing Institute and the Heilbronn Institute for Mathematical Sciences, which will be integrated into our programme.

I4. Having received an excellent training, the students will then impact powerfully on the world in their future fruitful careers, spreading excellence.

Impact on our Partners & ourselves.

I5. Direct impacts will be achieved by students engaging with, and working on projects with, our academic partners, with discipline-specific problems arising in engineering, education, medicine, economics, earth sciences, life sciences and geographical sciences, and our external partners Adarga, the Atomic Weapons Establishment, CheckRisk, EDF, GCHQ, GSK, the Office for National Statistics, Sciex, Shell UK, Trainline and the UK Space Agency. The students will demonstrate a wide range of innovation with these partners, will attract engagement from new partners, and often provide attractive future employment matches for students and partners alike.

Wider Societal Impact

I6. COMPASS will greatly benefit the UK by providing over 55 highly trained PhD graduates in an area that is known to be suffering from extreme, well-known, shortages in the people pipeline nationally. COMPASS CDT graduates will be equipped for jobs in sectors of high economic value and national priority, including data science, analytics, pharmaceuticals, security, energy, communications, government, and indeed all research labs that deal with data. Through their training, they will enable these organisations to make well-informed and statistically principled decisions that will allow them to maximise their international competitiveness and contribution to societal well-being. COMPASS will also impact positively on the wider student community, both now and sustainably into the future.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023569/1 31/03/2019 29/09/2027
2268657 Studentship EP/S023569/1 22/09/2019 21/09/2023 Dominic Milo Owens
2266494 Studentship EP/S023569/1 22/09/2019 15/03/2024 Daniel James Williams
2266564 Studentship EP/S023569/1 22/09/2019 21/09/2023 Andrea Becsek
2266490 Studentship EP/S023569/1 22/09/2019 21/09/2023 Michael Charles Whitehouse
2266135 Studentship EP/S023569/1 22/09/2019 21/09/2023 Douglas Corbin
2585444 Studentship EP/S023569/1 22/09/2019 21/09/2023 Alessio Zakaria
2266439 Studentship EP/S023569/1 22/09/2019 21/09/2023 Jake Daniel Spiteri
2266418 Studentship EP/S023569/1 22/09/2019 15/12/2023 Alexander Donald Modell
2438224 Studentship EP/S023569/1 20/09/2020 19/09/2024 Daniel Ward
2437902 Studentship EP/S023569/1 20/09/2020 19/09/2024 Shannon Williams
2438032 Studentship EP/S023569/1 20/09/2020 19/09/2024 Edward Davis
2437813 Studentship EP/S023569/1 20/09/2020 19/09/2024 Sam Stockman
2438023 Studentship EP/S023569/1 20/09/2020 20/12/2024 Euan Enticott
2437834 Studentship EP/S023569/1 20/09/2020 19/09/2024 Conor Newton
2438039 Studentship EP/S023569/1 20/09/2020 19/09/2024 Conor Crilly
2437825 Studentship EP/S023569/1 20/09/2020 19/09/2024 Jack Simons
2437930 Studentship EP/S023569/1 20/09/2020 19/09/2024 Georgina Mansell
2437392 Studentship EP/S023569/1 20/09/2020 20/12/2024 Anna Mary Gray
2592871 Studentship EP/S023569/1 30/09/2021 18/09/2025 Emerald Emerald Dilworth
2597535 Studentship EP/S023569/1 30/09/2021 18/09/2025 Harry Tata
2662077 Studentship EP/S023569/1 30/09/2021 18/09/2025 Ben Griffiths
2593163 Studentship EP/S023569/1 30/09/2021 18/09/2025 Edward Milsom
2592959 Studentship EP/S023569/1 30/09/2021 18/09/2025 Josh Givens
2597521 Studentship EP/S023569/1 30/09/2021 18/09/2025 Ettore E Fincato
2592879 Studentship EP/S023569/1 30/09/2021 18/09/2025 Hannah Joelle Sansford
2597525 Studentship EP/S023569/1 30/09/2021 18/09/2025 Daniel Milner
2592814 Studentship EP/S023569/1 30/09/2021 18/09/2025 Dominic Jay Broadbent
2597860 Studentship EP/S023569/1 30/09/2021 18/09/2025 Tennessee Hickling
2741133 Studentship EP/S023569/1 30/09/2022 17/09/2026 Dylan Dijk
2741510 Studentship EP/S023569/1 30/09/2022 17/09/2026 Xinrui Shi
2741439 Studentship EP/S023569/1 30/09/2022 17/09/2026 Rachel Wood
2740713 Studentship EP/S023569/1 30/09/2022 17/09/2026 Codie Wood
2741375 Studentship EP/S023569/1 30/09/2022 17/09/2026 Samuel Bowyer
2741521 Studentship EP/S023569/1 30/09/2022 17/09/2026 Emma Ceccherini
2740530 Studentship EP/S023569/1 30/09/2022 17/09/2026 Ben Anson
2741539 Studentship EP/S023569/1 30/09/2022 17/09/2026 Samuel Perren
2741534 Studentship EP/S023569/1 30/09/2022 17/09/2026 Emma Tarmey