Completed Computing & AI Psychology & Behaviour

Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics

In plain English

AI plain-English summary

Every time a city planner, doctor, or banker acts on data, that data becomes outdated—because the action itself changes the system being measured. This project tackles that fundamental problem: how to run ongoing experiments on complex, living systems like cities, companies, or human bodies, where you cannot pause the system to take a clean measurement. Current analytics treat data as static, but the world is not. When a bank adjusts its computing resources based on yesterday’s transaction patterns, those patterns shift. When a hearing aid is programmed using a single test, the user’s environment changes. The researchers will develop a closed-loop approach—a way to continuously update models as new data arrives and as the system responds to earlier decisions. The project will test these ideas in three concrete settings: personalising hearing aids in real time, analysing cancer data where treatment changes the patient’s biology, and adapting computing infrastructure for a major bank whose workload fluctuates unpredictably. If successful, the work could make data-driven decisions in medicine, finance, and urban management far more reliable—not by gathering more data, but by acknowledging that data and action are inseparable.

View original technical description
Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data, which can inform us of better ways to manage our cities, run our companies or develop new medicines. However, the 'elephant in the room' is that when we act on that data we change the world, potentially invalidating the older data. Similarly, when monitoring living cities or companies, we are not able to run clean experiments on them - we get data which is affected by the way they are run today, which limits our ability to model these complex systems. We need ways to run ongoing experiments on such complex systems. We also need to support human interactions with large and complex data sets. In this project we will look at the overlap between the challenge someone faces when coping with all the choices associated with booking a flight for a weekend away, and an expert running complex experiments in a laboratory. The project will test the core ideas in a number of areas, including personalisation of hearing aids, analysis of cancer data, and adapting the computing resources for a major bank.

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Researchers

Bjorn Jensen (Co-Investigator)Christos Anagnostopoulos (Co-Investigator)Craig Macdonald (Co-Investigator)Dirk Husmeier (Co-Investigator)Henrik Gollee (Co-Investigator)Iadh Ounis (Co-Investigator)Jinhyun Hong (Co-Investigator)John Williamson (Co-Investigator)Ke Yuan (Co-Investigator)Nikolaos Ntarmos (Co-Investigator)PETER TRIANTAFILLOU (Co-Investigator)Roderick Murray-Smith (Principal Investigator)Rónán Daly (Co-Investigator)Simon Rogers (Co-Investigator)

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Original classification

Research Grant

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