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Phone:: (023) 8059 8660
Email:: Adriane.Chapman@soton.ac.uk

Dr Age Chapman

Associate Professor of Computer Science

I am an Associate Professor of Computer Science, in the Web and Internet Science Group (WAIS), in Electronics and Computer Science at the University of Southampton. My research is in the area of database systems, focusing on using data appropriately and effectively. This involves solving problems that span the areas of databases, information discovery and retrieval, provenance, and algorithmic accountability. I have worked closely with the US Federal government, and influenced the Office of the National Coordinators (ONC) report on the usage of provenance within electronic health systems. I have advised the US Food and Drug Agency (FDA) , the National Geospatial-Intelligence Agency (NGA), and the Department of Homeland Security (DHS) on data management problems.

I serve on the steering committee of the Theory and Practice of Provenance (TaPP) and ProvenanceWeek. I am the recipient of the 2016 ACM SIGMOD Test of Time Award for my work on provenance. I run a Science Fair for the local primary schools to encourage the joy of research in budding scientists.

To apply for a PhD with me, please follow the ECS PhD application process.

Research

Publications

Teaching

Contact

Research interests

Artificial Intelligence and Data Science are making huge strides in improving the human condition, helping us to make decisions and utilize resources better. We are creating a new economy - the data economy. In support of this, my research revolves around how to ensure that data required for artificial intelligence and data science can be found and used appropriately. This includes:

Dataset retrieval: How can we discover datasets and rapidly assess which is suitable for a given task?
Data improvement: Can we provide additional information that allows data to be better understood, such as provenance and annotations?
Appropriate data usage: How can we ensure that the data chosen is used responsibly? To support algorithmic accountability, can we provide mechanisms that allow end users to understand the algorithmic impact of their data choices (e.g. to support the machine learning algorithms in the criminal justice system). Can we ensure that users's consent on their personal data is honored?

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Articles

Book Chapter

Heinis, T., & Chapman, A. (2017). Provenance Storage. In L. Liu, & M. T. Ozsu (Eds.), Encyclopedia of Database Systems Springer New York. https://doi.org/10.1007/978-1-4899-7993-3_80746-1

Conferences

Patents

Chapman, A., Mork, P., Samuel, K., Vayndiner, I., & Moore, D. (2013). Anomaly detection for database systems Anomaly detection for database systems. (Patent No. 8,504,876).
Chapman, A., Allen, M. D., Blaustein, B., & Seligman, L. (2012). Capturing Provenance Data Within Heterogeneous Distributed Communications Systems Capturing Provenance Data Within Heterogeneous Distributed Communications Systems. (Patent No. 12/917,891).

Report

Chapman, A., Blaustein, B., Seligman, L., & Allen, M. D. (2010). Provenance Capture and Use: A Practical Guide. The MITRE Corporation.

Thesis

Chapman, A. P. (2008). Incorporating provenance in database systems. University of Michigan.

Professional Development Game Design and Development Supervisor for: Part III Projects, Part IV Group Design Projects, MSc Projects and PhD students Previous teaching includes: Software Engineering Group Project (2018)

Dr Age Chapman
Building 58
Room 58/3087

Room Number : 32/3023

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