Dr. Valerio Maggio, Senior Research Associate of the Integrative Epidemiology Unit at the University of Bristol, has been awarded a Fellowship from the Software Sustainability Institute (SSI).
The focus of his fellowship will be on Privacy-Enhancing technologies for Machine Learning. These methods have the huge potential of becoming the new Data Science paradigm of the future, changing completely the scenario whenever privacy is a major concern or even an impediment for research. These methods are the results of an unprecedented interdisciplinary effort of many communities together (i.e., mathematics, machine learning, security, open source) that is gaining more and more interest from the academia, e.g. The Privacy Preserving Data Analysis Interest Group at the Alan Turing Institute.
With this fellowship, Dr. Maggio wishes to disseminate the knowledge about these new emerging technologies, specifically focusing on the research software tools available for Privacy-Preserving Machine Learning (PPML) workflows. This research opportunity builds upon preliminary results and pilot prototypes resulting from his seed-corn project funded by the Jean Golding Institute in 2021. Dr. Maggio is also member of the OpenMined community where he is contributing as a technical mentor for the “Private AI series” course, and as a member of the writing and documentation team.
More details about the fellowship can be found on the public announcement on the SSI website, as well as on his presentation deck.
Dr Skatova’s programme will focus on developing methods to analyse shopping data to improve population health. Digital technology opens up a new era in the understanding of human behaviour and lifestyle choices, with people’s daily activities and habits leaving ‘footprints’ in their digital records. For example, when we buy goods in supermarkets and use loyalty cards to obtain benefits (e.g., future discounts), the supermarket records our purchases and creates a representation of our habits and preferences.
Until now the use of ‘digital footprint’ data has mostly been limited to private companies to track sales of their products, and to target marketing and promotions. Changes in Data Protection law in the UK, mean the public can now access and donate their data for academic research. Shopping history data are an extremely rich source of information for population health research as it can provide granular, objective data on real world choices and behaviours. When shopping history data are used in a privacy preserving and ethical manner, these data can be utilised for public good, benefiting health research, helping to understand how everyday behaviours and lifestyle choices impact health and social outcomes.
Dr Skatova, based in the Population Health Sciences Department at Bristol Medical School, received a Turing Fellowship and project funding that built the basis for her £1.4m UKRI Future Leader Fellowship that will link transaction data to other environmental and health records collected by the Avon Longitudinal Study of Parents and Children.
The ultimate goal of the study is to put large commercial datasets — such as shopping history data — at the service of the public healthcare through contributing to early detection of diseases, developing and testing targeted interventions, and contributing to the evidence-based healthcare and health research.