Projects
Model criticism in multi-agent systems
Enabling autonomous agents to reason about the adequacy of models used to predict the actions of
other agents
Data science toolkit for explorable data visualisations
Helping scientists understand data analyses by connecting parts of visualisations to the data they depend on
Analysing social and geographic datasets
Mapping the available tools and techniques for qualitatively understanding human behaviour, in the context of social dynamics
Data safe havens in the cloud
Developing a policy and process framework for secure environments for productive data science research projects at scale
Donating personal transactional data for research
Investigating the public acceptability of using commercial transactional data in public health research
Programmes
Publications and software
Data Study Group Final Report: Imperial College London, Los Alamos National Laboratory, Heilbronn Institute
Developing data science tools for improving enterprise cyber-security
Data Study Group team. (2019, November 29). Data Study Group Final Report: Imperial College London, Los Alamos National Laboratory, Heilbronn Institute. Zenodo. http://doi.org/10.5281/zenodo.3558251
Formalising Sigma-Protocols and Commitment Schemes using CryptHOL
Machine-checked proofs of security are important to increase the rigour of provable security. In...
David Butler, Andreas Lochbihler, David Aspinall, Adria Gascon, Formalising Sigma-Protocols and Commitment Schemes using CryptHOL, Cryptography ePrint Archive, Report 2019/1185, https://eprint.iacr.org/2019/1185.pdf
Sigma Protocols and Commitment Schemes
We use CryptHOL to formalise commitment schemes and Sigma-protocols. Both are widely used fundamental...
David Butler and Andreas Lochbihler, Sigma Protocols and Commitment Schemes, Archive of Formal Proofs, October 2019
BEIS and DCMS consultation on Smart Data
Consultation In 2018, the Department for Business, Energy and Industrial Strategy (BEIS) published its...
Multi-Party Computation
We use CryptHOL to consider Multi-Party Computation (MPC) protocols. MPC was first considered by...
David Aspinall and David Butler. 2019. Multi-Party Computation. Archive of Formal Proofs.
On the Formalisation of Σ-Protocols and Commitment Schemes
There is a fundamental relationship between Σ-protocols and commitment schemes whereby the former can...
Butler D., Aspinall D., Gascón A. (2019) On the Formalisation of Σ-Protocols and Commitment Schemes. In: Nielson F., Sands D. (eds) Principles of Security and Trust. POST 2019. Lecture Notes in Computer Science, vol 11426. Springer, Cham
Articles
Time to enlist smartphones against coronavirus
How smartphone location data could be used to help track the spread of COVID-19
Wednesday 18 Mar 2020
The Turing’s Finance and Economics Programme responds to the Smart Data consultation
Three things you need to know
Thursday 08 Aug 2019
Can data trusts be the backbone of our future AI ecosystem?
The interest in exploring the concept of ‘data trusts’ - a way of sharing...
Wednesday 03 Oct 2018
Turing researchers call for changes to data protection law to protect consumer and individual privacy
Data protection law (meant to protect people’s privacy, identity, reputation and autonomy) is currently...
Friday 14 Sep 2018
Events
The BigScience philosophy of building open AI
Tuesday 14 Jun 2022
Time: 16:00 - 17:00
On the dangers of stochastic parrots
Thursday 08 Jul 2021
Time: 16:00 - 17:15
Robotics and AI for health and social care
Monday 28 Oct 2019 - Tuesday 29 Oct 2019
Time: 10:00 - 17:30
Data Debate: Cities – smart or sinister?
Monday 21 Oct 2019
Time: 19:00 - 21:30
Responsible Human-Machine Teaming
Tuesday 01 Oct 2019
Time: 09:45 - 17:30
Visualization for data science and AI
Friday 13 Sep 2019
Time: 09:45 - 18:30
Sharing your healthcare data safely
Thursday 28 Mar 2019
Time: 10:30 - 16:00
Data and inequality: Towards data justice?
Monday 09 Jul 2018
Time: 16:00 - 17:30
Data and inequality - we, the users: technologies of our future and who they serve
Thursday 21 Jun 2018
Time: 16:00 - 17:30
Impact stories
Interest groups
Visualization
How can we gain insight into ever-growing datasets and see the reasoning behind automated AI decision-making?
Machine learning in finance
What are the key challenges of adopting artificial intelligence methods in the financial services industry?
Privacy-preserving data analysis
How can we enable personal data to be shared without compromising our privacy?
People
Dr Antonella Delmestri
Turing Fellow
Dr Yi Yu
Turing Fellow
Professor Jeremy Crampton
Turing Fellow
Dr Richard Allmendinger
Turing Fellow
Professor Henry Reeve
Turing Fellow
Dr Martin Lotz
Turing Fellow
Dr Jennifer Collins
Turing Fellow
Professor David Parker
Turing Fellow