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CIRSS Seminar - The future of medicine and health care is Data-Driven, Predictive, Preventive, Personalised, and Participative

Friday, March 19, 2021
4pm - 5pm


Event Details

Session leaders: Paolo Missier, Professor of Scalable Data Analytics with the School of Computing at Newcastle University
Description: This "DD+P4" mantra summarises a vision where everybody can afford their best chance of a healthy, good quality life for longer, through a combination of self-monitoring, early detection of diseases, and personalised (clinical, lifestyle) interventions at low-cost and at population scale. This vision translates into an exciting long-term, multi-disciplinary research agenda at the intersection of health science, data science and AI, data engineering, and computer science.

In this talk I will try and provide an overview of challenges encountered and lessons learnt (so far) on our initial journey in this space. The script starts with excitement at potential of using AI for healthcare, then takes a dip when we hit the complexity and untidiness of the underlying data, but picks up again when we realise that data and metadata engineering has a lot to offer to realise some of the T's in this game: Transparency, Traceability, Trustworthiness (and possibly more).

Paolo Missier is Professor of Scalable Data Analytics with the School of Computing at Newcastle University and currently a Fellow (2018-2020) of the Alan Turing Institute, UK's National Institute for Data Science and Artificial Intelligence.

With a background in traditional databases and data management, Paolo's research has touched on Data and Information Quality, web semantics, workflow-based infrastructure for e-science, and data provenance. Past and current funded projects include work on scalable processing of genomics pipeline on the cloud (NIHR-BRC), on optimising analytics pipelines in response to changes in data (EPSRC), and more. He recently oriented his research portfolio to address the challenges and opportunities of data science in health care, with contributions on the search for "digital biomarkers" from self-monitoring devices and applications of machine learning to predictive modelling for covid. His interests expand to the management and exploitation of data provenance in data science pipelines, and in algorithmic fairness.

Paolo has been a Research Scientist at Bell Communications Research, USA (1994-2001), and as a Research Fellow at the University of Manchester, School of Computer Science (2004-2011) where he got his PhD in 2008. At Newcastle he leads the School of Computing's post-graduate academic teaching on Big Data Analytics. He is Sr. Associate Editor for the ACM Journal on Data and Information Quality (JDIQ).