We recently spoke with the Cantab Capital Institute for the Mathematics of Information (CCIMI) about their projects and research. They explained why funding for mathematics research is so essential, how it can be applied, and what sets the CCIMI apart from other research institutes.
In this introduction to the CCIMI, we speak to Hamza Fawzi, Director of the Institue, Colm-Cille Caulfield, Head of Department of Applied Mathematics and Theoretical Physics, and some of the students involved with the CCIMI’s research and teaching.
Established by the Turner Kirk Trust in 2016, the CCIMI is part of the Faculty of Mathematics at the University of Cambridge, combining the disciplines of pure, theoretical, and applied mathematics and statistics. The Institute works to find new and effective ways of interpreting data, which can be applied to a whole host of real-life situations including engineering, healthcare, and policy.
Ewan Kirk, Founder of the Turner Kirk Trust, has had a deep interest in mathematics throughout his education and career. Through the Trust, he has focused on providing funding for research into mathematics as its own endeavour, rather than requiring research to have a pre-determined application or problem that it aims to solve. The Turner Kirk Trust has also supported a range of other STEM projects, such as the Turner-Kirk UAV Research Support Programme at the University of Southampton in 2019.
The Turner Kirk Trust provided a £5 million philanthropic donation to the Mathematics Faculty at the University of Cambridge the CCIMI in 2015, and has since continued to support its research and teaching.
The Cantab Capital Institute for the Mathematics of Information is hosted within the Faculty of Mathematics of the University of Cambridge and accommodates research activity on fundamental mathematical problems and methodology for understanding, analysing, processing and simulating data. Data science research performed in the Institute is on the highest international level, aiming to extract the relevant information from large- and high-dimensional data with a predictable certainty.
