Uncertainty Quantification

EQUIP is a 2.6M EPSRC Programme Grant which will tackle a number of key challenges arising in the solution of statistical inverse problems, guided by applications arising in subsurface geophysics.

Data-centric Engineering

The next decade will see step changes in data-driven technology, impacting all aspects of engineering and industry. In preparation, the Lloyd’s Register Foundation and The Alan Turing Institute have partnered on a major initiative to address new challenges in data-centric engineering.  The programme for data-centric engineering is based at The Alan Turing Institute and is led by Professor Mark Girolami.  The programme brings together world-leading academic institutions and major industrial partners from across the engineering sector.

Insights into Cities (ICONIC)

ICONIC is an ambitious project, funded by EPSRC, that combines mathematical modeling, statistical inference, and scientific computing in order to develop rigorous and efficient uncertainty quantification tools, which will be targeted at mathematical models and data sets arising from the future cities arena, focusing on crime, security and resilience.  The project lies at the interface between applied mathematics, statistics, and high performance computing. Mathematics, the language of science, is used to model natural and technological systems. Statistics helps us to make sense of data. By developing algorithms and implementing them on high performance computers, notably using many-core processors such as graphical processing units (GPUs), we make these ideas concrete.

Software Structures

End-user adaptation of software structure offers a potentially major benefit: giving people the means to customise a program to suit individual needs, interests and contexts. However, in their different ways, users, analysts and developers are all challenged by complexities and costs when handling structural variation. Developers and analysts struggle to understand and work with what is happening in deployments. Each user is often unsupported in understanding what changes to software structure and its use might work well in terms of either objective functionality or subjective experience.