I am a Professor of Computer and Data Science, specialising in Data and Knowledge management, Data Science and Engineering, with applications mainly to the Health domain.

Current research:

1. Improving Data Science to improve Science: 

Exploring how to make Data Science responsible and trustworthy by instrumenting complex data engineering and data processing pipelines, for accountability and explanations.  This includes exploring new techniques for collecting and making sense of data provenance and audit traces and enhancing, specifically within the emerging context of Data-Centric AI. Recently (2021-23) we have been developing DPDS (“Data Provenance for Data Science”), a tool for collecting the provenance of dataframes that are manipulated using python pandas as part of Data Science pipelines.

2. Health Data Science at scale for participatory, preventative, personalised care:

• The future of healthcare is not only data-driven, it is also participatory: we explore health technology and models aimed at motivating and empowering individuals to engage with their own health, enabling disease prevention and early onset detection. We envision "AI-enabled", trusted personal advisors who have current knowledge of our health trajectory and are able to track risk factors without the need for periodic clinical visits.

• Exploring the potential of integrated care systems to prevent and better manage multiple long-term conditions. What models and tools can we provide to best support the patient-clinician-carer ecosystem through the life course, and how can we measure and then improve the Quality of Life of multi-morbid chronic patients?

Past research:

(a) Workflows for Science (aka "scientific workflows") (b) ReComp: optimising the repeated execution of data analytics pipelines specifically for Life Sciences / Genomics, (c) porting genomics pipelines to the cloud, (d) digital phenotyping of metabolic diseases (Type 2 Diabetes) from wearable accelerometers.

Community contributions: Modelling Provenance.

My contributions in the area of data provenance culminated with the publication of the W3C PROV standard data model for Provenance interoperability. This also generated a 4* REF 2021 Impact Case Study for Newcastle University.

Prior to joining Birmingham in 2024, from 2011 to 2023 I have been a Lecturer, Reader, and Professor in the School of Computing at Newcastle University. I have been a Fellow (2018-2023) of the Alan Turing Institute, UK's National Institute for Data Science and Artificial Intelligence.

My qualifications include first and MSc degrees in Computing at Universita' di Udine, Italy (1990), a further MSc in Computer Science from University of Houston (USA), and a PhD in Computer Science from the University of Manchester (2008), with focus on infrastructure to support data quality in workflow programming for scientific applications.

I have been leading post-graduate teaching on Data Engineering for AI, and introduction to Predictive Analytics at the undergraduate level.

Since 2016 I have been Sr. Associate Editor for the ACM Journal on Data and Information Quality (JDIQ).

2008: Ph.D. in Computer Science, University of Manchester, UK: Modelling and Computing Information Quality in e-Science (Supervisor Dr. Suzanne Embury)

1993: M.Sc. in Computer Science, University of Houston, Tx, USA.

1990: B.Sc. and M.Sc. in Computer Science from Universita’ di Udine, Italy.