I am very pleased to have been invited to join the Peer Review College for the UKRI (UK Research and Innovation) Future Leaders Fellowships.
UK Research and Innovation (UKRI) ‘is the national funding agency investing in science and research in the UK. Operating across the whole of the UK with a combined budget of more than £6 billion, UKRI brings together the 7 Research Councils, Innovate UK and Research England’.
‘The UK Research and Innovation Future Leaders Fellowships (FLF) will grow the strong supply of talented individuals needed to ensure that UK research and innovation continues to be world class.’
I have been invited to become a member of the EPSRC Associate Peer Review College.
The EPSRC has a budget of £0.8 billion, which it uses to fund research in engineering and the physical sciences.
These research grants are awarded on a competitive basis, and a pool of reviewers decide on which proposals should be funded. The process should be an interesting one, particularly for keeping up to date and influencing priority research areas for UK science and engineering.
This paper, published with colleagues from Warwick University and Cambridge University: Petra Vertes, Ruth Nicol, Sandra Chapman, Nicholas Watkins, and Edward Bullmore was the result of inter-disciplinary work funded by the EPSRC – the Engineering and Physical Sciences Research Council (Grant number EP/H02395X/1). We investigated the similarities in the network structure financial markets and brain networks.
Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties.