UKRI AI Centre for Doctoral Training in Foundational Artificial Intelligence

Lead Research Organisation: University College London
Department Name: Computer Science


The UK has had remarkable success in producing new algorithms in Artificial Intelligence. This has driven two processes -- one is the adoption of existing large-scale data analysis tools to industry problems. The other is the acquisition by large tech companies of UK talent, with the intention that it will significantly shape the future commercial landscape.

Whilst there has been dramatic recent progress in AI, the systems are still far from being universally useful. The tech giants are well aware of this and are investing heavily in teams and algorithms that can address these fundamental AI challenges.

It is vital that the UK retains the ability to create new AI technologies and does not become merely a consumer and user of AI technologies produced by others. This training centre will spearhead this effort by addressing the core research problems and training students to become the next generation scientists and entrepreneurs.

Planned Impact


Science : A key beneficiary is the science community itself. The challenge of advancing AI to incorporate more knowledge about the human world, the objects therein and to thus be able to interact with us in more natural ways, is difficult but exciting and potentially hugely rewarding. As an example, a machine that can read and comprehend texts and form a unified representation and understanding of the information within those scattered texts has the potential to transform areas such as medicine as otherwise unconnected ideas can be brought together to form new insights. Making new AI technologies that can explain how they take their decisions and rationally justify them is of great importance is certain sectors such as medicine.

Society : Making explainable and transparent AI is clearly of great concern to the general public. This is a relatively new area of research and the CDT is well placed to make progress on this, particularly in partnership with our partner the Alan Turing Institute. DeepMind have a research programme that is attempting to address this and we expect to benefit from interactions with them as well as other researchers.

Startup Companies : We expect a number of AI startups to emerge from the CDT. This will directly benefit the UK economy by providing employment and investment in the UK. The UCL Computer Science Department has a good track record of making AI startups, with the management of the CDT being themselves successful AI entrepreneurs. There is significant infrastructure in place to help train and support PhD students to generate such companies, with a wealth of local expertise from academics and other students that have successfully achieved this.

Companies : CDT graduates are expected to be leaders in new AI technologies, going well beyond the application of existing data analysis tools. For example, we expect CDT graduates to provide their future employers will the ability to design new products based on the highly specialised training the students will have received. Through our CDT industry placement scheme, companies will also benefit from having students will world-leading AI talent to help them drive innovation.

Government and Public Policy : UCL runs a public policy placement scheme with UK government departments. This provides an opportunity for students to contribute to policy and decision making of potentially national significance.

Medicine : We expect to make advances in healthcare which will make more efficient use of hospital resources. We will also make advanced algorithms that will improve medical diagnosis and eventual treatment of disease.


Our industry advisory board will ensure that research and teaching remain relevant.

The AI Centre at UCL will regularly host events and invite industry to view the outputs of research from the Centre. The CDT will be embedded within this larger AI Centre and students will therefore have the opportunity to engage with and get expose to industry.

Through our annual AI pitch day, investors will have the opportunity to fund student ideas. UCL has a unit dedicated to supporting AI spinouts, including seed funding, mentoring and space.

The CDT aims to place all research and results in the public domain. In addition to academic publication channels (the major AI conferences and journals) we will reach out to more popular science and media sources (our UCL advisor Jack Stilgoe is a Guardian columnist on ethical AI).

It is standard in AI research to produce public software alongside publications. To maximise impact we will mandate (wherever possible) that software is openly available.

The CDT will maintain a high profile website and a manage a corresponding social media profile. We have a dedicated media officer in the Computer Science Department at UCL who will deliver this.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021566/1 31/03/2019 29/09/2027
2267930 Studentship EP/S021566/1 22/09/2019 21/09/2023 Samuel Cohen
2251002 Studentship EP/S021566/1 22/09/2019 21/09/2023 Jas Semrl
2251012 Studentship EP/S021566/1 22/09/2019 21/09/2023 Yihong Chen
2250981 Studentship EP/S021566/1 22/09/2019 21/09/2024 Dan Stoddart
2251009 Studentship EP/S021566/1 22/09/2019 21/09/2023 Felix Richard Biggs
2251565 Studentship EP/S021566/1 22/09/2019 21/09/2023 Jingwen Wang
2250955 Studentship EP/S021566/1 22/09/2019 23/12/2023 Augustine Mavor-Parker
2253986 Studentship EP/S021566/1 22/09/2019 31/12/2023 Luca Morreale
2251578 Studentship EP/S021566/1 22/09/2019 21/09/2023 Changmin Yu
2252836 Studentship EP/S021566/1 22/09/2019 21/09/2023 Jakob Zeitler
2408233 Studentship EP/S021566/1 26/09/2020 29/09/2024 Denis Hadjivelichkov
2408383 Studentship EP/S021566/1 27/09/2020 29/09/2024 Oscar Thomas Key
2409434 Studentship EP/S021566/1 27/09/2020 31/12/2024 Oliver Slumbers
2408098 Studentship EP/S021566/1 27/09/2020 29/09/2024 Reuben Adams
2408309 Studentship EP/S021566/1 27/09/2020 29/09/2024 Jean Heidar Kaddour
2408394 Studentship EP/S021566/1 27/09/2020 29/09/2024 Robert Kirk
2409398 Studentship EP/S021566/1 27/09/2020 29/09/2024 Antonin Florentin Schrab
2409446 Studentship EP/S021566/1 27/09/2020 29/09/2024 Sicelukwanda Njabuliso Zwane
2408220 Studentship EP/S021566/1 27/09/2020 29/09/2024 Yue Feng
2409392 Studentship EP/S021566/1 27/09/2020 29/09/2024 Yicheng Luo
2408302 Studentship EP/S021566/1 27/09/2020 29/09/2024 Alex Hawkins-Hooker
2419792 Studentship EP/S021566/1 30/09/2020 29/09/2024 Mirgahney Husham Mohamed
2425997 Studentship EP/S021566/1 30/09/2020 29/09/2024 Linqing Liu
2409437 Studentship EP/S021566/1 30/09/2020 29/09/2024 Yuchen Zhu
2570923 Studentship EP/S021566/1 30/09/2021 29/09/2025 Emilio Dara McAllister Fognini
2570649 Studentship EP/S021566/1 30/09/2021 29/09/2025 Laura Eline Ruis
2570828 Studentship EP/S021566/1 30/09/2021 29/09/2025 Zak Stefan Morgan
2570927 Studentship EP/S021566/1 30/09/2021 29/09/2025 Harry Jake Cunningham
2570758 Studentship EP/S021566/1 30/09/2021 29/09/2025 Harry Songhurst
2570916 Studentship EP/S021566/1 30/09/2021 29/09/2025 Aengus Lynch
2574460 Studentship EP/S021566/1 30/09/2021 29/09/2025 Nikolay Dagaev
2584610 Studentship EP/S021566/1 30/09/2021 29/09/2025 Laura Eline Ruis
2570783 Studentship EP/S021566/1 30/09/2021 29/09/2025 Akbir Khan
2570920 Studentship EP/S021566/1 30/09/2021 29/09/2025 Rokas Bendikas
2584277 Studentship EP/S021566/1 01/11/2021 31/10/2025 Laura Eline Ruis
2575973 Studentship EP/S021566/1 01/11/2021 31/10/2025 Mariia Naslidnyk