Closed-Loop Data Science
Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data. This data can inform us of better ways to manage our cities, run our companies or develop new medicines. But there’s a fundamental problem; when we act on this data we change the state of the system, potentially invalidating the data that we are learning from.
This effect is seen on complex closed-loop systems such as living cities, companies or the human body.
The £3M, four-year Closed-Loop Data Science Project will see Glasgow computing scientists work with project partners with backgrounds ranging from mathematics and statistics to engineering and urban studies. The goal is to improve the acquisition, analysis, and exploitation of complex data by viewing data science systems explicitly as dynamic systems involving feedback, delays and uncertainty.
When monitoring cities or companies, we are not able to run clean experiments on them. Instead we get data which is affected by the way they are run today, and this limits our ability to model these complex systems. To truly learn from and predict the effects of such complex systems, we need ways to run ongoing experiments on them.
The core ideas that are developed through the project will be tested within a number of areas, including personalisation of hearing aids, support for travel planning, analysis of cancer data, and media recommendation systems.
Academic partners
- School of Computing Science: Information, Data and Analysis (IDA)
- Glasgow Polyomics
- Urban Big Data Center
- University of Warwick
Industrial partners
- Moodagent
- Widex A/S
- Thales
- Telefonica
- Aegean Airlines
- Skyscanner
- J.P. Morgan
- Amazon Web Services
Additional support
- DataLab Scotland