Research

My research is centred around developing and applying methods from other disciplines to solving complex spatial problems particularly in the area of Urban Analytics.  I am interested in artificial intelligence, machine learning, data analytics and visualisation.  A current list of funded projects is below:

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Bringing the Social City to the Smart City (ESRC-Turing Fellowship): This three year fellowship looks at (i) methodologies for uncovering hidden patterns and processes in spatio-temporal systems; (ii)  casual relationships in populations and (iii) explores uncertainty in individual-based modelling for city simulation.

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Systems Science in Public Health and Health Economics Research (SIPHER) : SIPHER vision is a shift from health policy to health public policy.  Along with Dr Nik Lomax, I am responsible for the data management and micro-modelling work streams of this 5 year UKPRP consortium

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Behavioural, ecological and socio-economic tools for modelling agricultural policy (BESTMAP – H2020):  My role in this project is to devise ways to scale up ABMs from local to national levels.

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Consumer Data Research Centre (ESRC):The CDRC seeks to develop new approaches to social science research which are needed to exploit new sources of consumer data. I hold the post of Director of Innovation.

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Below are Turing projects that I am involved with – more information can be found via my Turing page.

Understanding and Quantifying Uncertainty in Agent-Based Models for Smart City Forecasts: (Turing) Developing methods that can be used to better understand uncertainty in individual-level models of cities

Capturing relationships between individuals: Integrating Causal Inference and Agent-based modelling: (Turing). This project will connect ongoing work in casual inference modelling to agent-based simulations to robustly capture and simulate causal relationships between individuals.

Forecasting the future of policing (Turing): This project is in conjunction with UCL and The Met to explore the potential of ABM as a tool for forecasting demands in policing.  The PI is Dr Dan Birks (University of Leeds).

Quantifying Utility and Preserving Privacy in Synthetic Data (QUIPP):  This is a joint project with the Turing that is aims to generate synthetic versions of sensitive data sets that contain all the relationships and preserve individual privacy.

Real-Time Advanced Data assimilation for Digital Simulation of Numerical Twins on HPC (RADDISH): This project will perform the essential computational groundwork to allow researchers to apply DA methods to coupled human-environmental systems. The overall PI is Prof Serge Gullias (UCL).

Data Assimilation for Agent-based models (ERC): This project is devising new approaches to calibrate and validate ABMs in real-time, thereby improving the accuracy of short-term forecasts of social systems.  Model code and updates can be found on the following Github pages. The PI is Dr Nick Malleson.