EPSRC Centre for Doctoral Training in Health Data Science
Lead Research Organisation:
University of Oxford
Department Name: Computer Science
Abstract
Data science and artificial intelligence will transform the way in which we live and work, creating new opportunities and challenges to which we must respond. Some of the greatest opportunities lie in the field of human health, where data science can help us to predict and diagnose disease, determine the effectiveness of existing treatments, and improve the quality and affordability of care.
The Oxford EPSRC CDT in Health Data Science will provide training in:
- core data science principles and techniques, drawing upon expertise in computer science, statistics, and engineering
- the interpretation and analysis of different kinds of health data, drawing upon expertise in genomics, imaging, and sensors
- the methodology and practice of health data research, drawing upon expertise in population health, epidemiology, and research ethics
The training will be provided by academics from five university departments, working together to provide a coordinated programme of collaborative learning, practical experience, and research supervision.
The CDT will be based in the Oxford Big Data Institute (BDI), a hub for multi-disciplinary research at the heart of the University's medical campus. A large area on the lower ground floor of the BDI building will be allocated to the CDT. This area will be refurbished to provide study space for the students, and dedicated teaching space for classes, workshops, group exercises, and presentations.
Oxford University Hospitals NHS Foundation Trust (OUH), one of the largest teaching hospitals in the UK, will provide access to real-world clinical and laboratory data for training and research purposes. OUH will provide also access to expertise in clinical informatics and data governance, from a practical NHS perspective. This will help students to develop a deep understanding of health data and the mechanisms of healthcare delivery.
Industrial partners - healthcare technology and pharmaceutical companies - will contribute to the training in other ways: helping to develop research proposals; participating in data challenges and workshops; and offering placements and internships. This will help students to develop a deep understanding of how scientific research can be translated into business innovation and value.
The Ethox Centre, also based within the BDI building, will provide training in research ethics at every stage of the programme, and the EPSRC ORBIT team will provide training in responsible research and innovation. Ethics and research responsibility are central to health data science, and the CDT will aim to play a leading role in developing and demonstrating ethical, responsible research practices.
The CDT will work closely with national initiatives in data science and health data research, including the ATI and HDR UK. Through these initiatives, students will be able to interact with researchers from a wide network of collaborating organisations, including students from other CDTs. There will also be opportunities for student exchanges with international partners, including the Berlin Big Data Centre.
Students graduating from the CDT will be able to understand and explore complex health datasets, helping others to ask questions of the data, and to interpret the results. They will be able to develop the new algorithms, methods, and tools that are required. They will be able to create explanatory and predictive models for disease, helping to inform treatment decisions and health policy.
The emphasis upon 'team science' and multi-disciplinary working will help to ensure that our students have a lasting, positive impact beyond their own work, delivering value for the organisations that they join and for the whole health data science community.
The Oxford EPSRC CDT in Health Data Science will provide training in:
- core data science principles and techniques, drawing upon expertise in computer science, statistics, and engineering
- the interpretation and analysis of different kinds of health data, drawing upon expertise in genomics, imaging, and sensors
- the methodology and practice of health data research, drawing upon expertise in population health, epidemiology, and research ethics
The training will be provided by academics from five university departments, working together to provide a coordinated programme of collaborative learning, practical experience, and research supervision.
The CDT will be based in the Oxford Big Data Institute (BDI), a hub for multi-disciplinary research at the heart of the University's medical campus. A large area on the lower ground floor of the BDI building will be allocated to the CDT. This area will be refurbished to provide study space for the students, and dedicated teaching space for classes, workshops, group exercises, and presentations.
Oxford University Hospitals NHS Foundation Trust (OUH), one of the largest teaching hospitals in the UK, will provide access to real-world clinical and laboratory data for training and research purposes. OUH will provide also access to expertise in clinical informatics and data governance, from a practical NHS perspective. This will help students to develop a deep understanding of health data and the mechanisms of healthcare delivery.
Industrial partners - healthcare technology and pharmaceutical companies - will contribute to the training in other ways: helping to develop research proposals; participating in data challenges and workshops; and offering placements and internships. This will help students to develop a deep understanding of how scientific research can be translated into business innovation and value.
The Ethox Centre, also based within the BDI building, will provide training in research ethics at every stage of the programme, and the EPSRC ORBIT team will provide training in responsible research and innovation. Ethics and research responsibility are central to health data science, and the CDT will aim to play a leading role in developing and demonstrating ethical, responsible research practices.
The CDT will work closely with national initiatives in data science and health data research, including the ATI and HDR UK. Through these initiatives, students will be able to interact with researchers from a wide network of collaborating organisations, including students from other CDTs. There will also be opportunities for student exchanges with international partners, including the Berlin Big Data Centre.
Students graduating from the CDT will be able to understand and explore complex health datasets, helping others to ask questions of the data, and to interpret the results. They will be able to develop the new algorithms, methods, and tools that are required. They will be able to create explanatory and predictive models for disease, helping to inform treatment decisions and health policy.
The emphasis upon 'team science' and multi-disciplinary working will help to ensure that our students have a lasting, positive impact beyond their own work, delivering value for the organisations that they join and for the whole health data science community.
Planned Impact
In the same way that bioinformatics has transformed genomic research and clinical practice, health data science will have a dramatic and lasting impact upon the broader fields of medical research, population health, and healthcare delivery. The beneficiaries of the proposed training programme, and of the research that it delivers and enables, will include academia, industry, healthcare, and the broader UK economy.
Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.
Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.
Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.
Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.
Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.
Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.
Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.
Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.
Organisations
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S02428X/1 | 31/03/2019 | 29/09/2027 | |||
2279748 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/12/2023 | Odhran O'Donoghue |
2280883 | Studentship | EP/S02428X/1 | 30/09/2019 | 29/09/2023 | Xinchi Qiu |
2279625 | Studentship | EP/S02428X/1 | 30/09/2019 | 06/01/2024 | Jacob Armstrong |
2279773 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/12/2023 | Andres Tamm |
2280681 | Studentship | EP/S02428X/1 | 30/09/2019 | 29/09/2024 | Maxime Kayser |
2282001 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/12/2023 | Kangning Zhang |
2271697 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/03/2024 | Henrique Aguiar |
2279808 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/12/2023 | Claudia Vanea |
2280532 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/03/2024 | Cornelius Emde |
2279638 | Studentship | EP/S02428X/1 | 30/09/2019 | 31/03/2024 | Jonathan Campbell |
2432020 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Niall Taylor |
2431819 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Aleksandra Ziubroniewicz |
2432736 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Angus Nicolson |
2431522 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Jong Kwon |
2432652 | Studentship | EP/S02428X/1 | 30/09/2020 | 31/12/2024 | George Batchkala |
2873400 | Studentship | EP/S02428X/1 | 30/09/2020 | 31/12/2024 | Alexander Sauer |
2431966 | Studentship | EP/S02428X/1 | 30/09/2020 | 30/11/2024 | Adam Sturge |
2432289 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Alexander Sauer |
2294883 | Studentship | EP/S02428X/1 | 30/09/2020 | 31/12/2024 | Harriet Longley |
2432658 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Owen Dwyer |
2432026 | Studentship | EP/S02428X/1 | 30/09/2020 | 31/12/2024 | Lara Chammas |
2432401 | Studentship | EP/S02428X/1 | 30/09/2020 | 29/09/2024 | Ruby Wood |
2593890 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Eloise Ockenden |
2636068 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Abram Schonfeldt |
2593917 | Studentship | EP/S02428X/1 | 30/09/2021 | 31/12/2025 | Isobel Howard |
2633475 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Jiazheng Zhu |
2728935 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Ellen Visscher |
2594573 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Felix Wagner |
2633452 | Studentship | EP/S02428X/1 | 30/09/2021 | 30/03/2026 | Jiazheng Zhu |
2594554 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Lav Radosavljevic |
2599168 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Jiazheng Zhu |
2593955 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Ruben Weitzman |
2599185 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Abram Schonfeldt |
2593947 | Studentship | EP/S02428X/1 | 30/09/2021 | 31/12/2025 | Nasma Dasser |
2592417 | Studentship | EP/S02428X/1 | 30/09/2021 | 29/09/2025 | Ambre Bertrand |
2721657 | Studentship | EP/S02428X/1 | 01/01/2022 | 29/09/2026 | Zhi Yan Bo |
2722218 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Joshua Strong |
2722264 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Hermione Warr |
2721977 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Yasin Ibrahim |
2722208 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Thalia Seale |
2722269 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Katarina Vukosavljevic |
2722161 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Sara Matijevic |
2721975 | Studentship | EP/S02428X/1 | 30/09/2022 | 31/12/2026 | Charlotte Ibbeson |
2722183 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Pafue Nganjimi |
2721830 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Annie Qurat Ul Ain |
2721961 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Moritz Gogl |
2873398 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Thalia Seale |
2721784 | Studentship | EP/S02428X/1 | 30/09/2022 | 29/09/2026 | Robin Park |
2873920 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Emma Walker |
2873955 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Jeronee Jennycloss |
2873831 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Anna Bator |
2873909 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Angeliki Papathanasiou |
2873903 | Studentship | EP/S02428X/1 | 30/09/2023 | 30/03/2028 | Kacper Kapusniak |
2873914 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Owen Pullen |
2873843 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Lucy Greenwood |
2873918 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Emma Prevot |
2873841 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Rosario Evans Pena |
2876277 | Studentship | EP/S02428X/1 | 30/09/2023 | 29/09/2027 | Alexander Gruen |