AS&RU researchers, Dr Martine Barons, Dr Thais Fonseca and Prof Jim Smith have developed a decision support system (DiAGRAM) in collaboration with The National Archive (TNA) for the digital archiving community. The project – called “Safeguarding the nation’s digital memory” built on research by Prof Smith, Dr Manuele Leonelli and Dr Martine Barons to develop a probabilistic model to allow archives of different sizes and structures to assess risk levels for their digital collections and how the risk levels change under proposed interventions. TNA states that the nation’s digital heritage is rich, complex and fragile. This material – born-digital records (in a variety of formats), web archives, digitised archival materials – is under threat from rapidly evolving technology, outdated policies and a skills gap across the archives sector. To preserve this heritage for future generations, we must understand and navigate a vast and ever-shifting risk landscape. This tool, launched in November 2020, was developed with input with a number of partner archives and with input from archives across the world, including Brazil, USA, Australia and more. It was shortlisted as a finalist in the worldwide Digital Preservation Coalition (DPC) Awards and has already received a request to translate into Turkish!

There were nominations from 14 countries for the DPC Awards. The winners in the research & innovation category were ‘Introducing levels of born digital’ https://www.dpconline.org/blog/introducing-levels-born-digital-access .
 
https://www.nationalarchives.gov.uk/information-management/manage-information/preserving-digital-records/research-collaboration/safeguarding-the-nations-digital-memory/ https://nationalarchives.shinyapps.io/DiAGRAM-dev/

AS&RU Director, Dr Martine Barons, features in the October special issue on Knowledge Exchange of Mathematics Today. Knowledge exchange - a process which brings together academic staff, users of research and wider groups and communities to increase the impact of research – is important to ensure the benefit of research are enjoyed by society as a whole. Knowledge exchange in the mathematical sciences is, perhaps, the hardest of all, as there are significant social barriers as even the well-educated can perceive mathematics as a difficult subject that exposes the weakness of their intelligence and reduces their confidence in their intellectual capacity. In this Special issue, eight people with strong records in knowledge exchange are interviewed, including Dr Barons, and the role of Knowledge Transfer Partnerships is explored. Even during lockdown, virtual knowledge exchange events, akin to study groups, have been operating very successfully and bringing the power of the mathematical sciences to bear on society’s pressing problems.
https://ima.org.uk/15078/interview-with-dr-martine-barons-cmath-mima/

Congratulations to Alejandra Avalos-Pacheco who the Savage Award of the International Society for Bayesian Analysis in the category Applied Methodology for her 2019 PhD thesis “Factor regression for dimensionality reduction and data integration techniques with applications to cancer data.” Dr Avalos-Pacheco is now a postdoctoral fellow in Statistics at Harvard Medical School.

Congatulations also to François-Xavier Briol whose Warwick PhD thesis "Statistical Computation with Kernels” received an Honorable Mention in the “Theory and Methodology” category. Dr Briol is now a Lecturer in the Department of Statistical Science at University College London.

The main goal of this proposal is to lay the theoretical foundations for the analysis of the stability of Markov chains with heavy-tailed targets, focusing on the processes that underpin many randomised algorithms used in practice. This project is an international collaboration between the universities of Warwick and Stanford, made possible by the EPSRC-NSF Lead
Agency agreement in mathematics. As such, the project presents a unique opportunity to combine the US expertise in theoretical Operations Research with the UK's capability in Computational Statistics, resulting in novel methodology for the analysis of the convergence of Markov chains with heavy-tailed targets.

This theoretical proposal, based on probabilistic methods (e.g. martingale theory, coupling and Lyapunov functions) will have immediate impact within Probability, most notably on the theory of the stability of stochastic systems. In time, however, this work is expected to have impact far beyond applied probability in a number of sub-areas of computational statistics and machine learning where heavy-tailed targets arise