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AI & Machine Learning

We develop new Machine Learning and Statistical methods that use mass behavioural data, to generate new research insights.

Human Behaviour

Our research focuses on behavioural analytics, methods that use mass datasets to help better understand the behaviours that underpin our daily lives.

Social Good

Our projects predominantly target social good, from modelling food poverty and health in the UK, to predicting risk of perinatal mortality in East Africa.

Transformative Research

About the Research Centre

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N/LAB is a centre of excellence at the University of Nottingham in behavioural analytics and social data science. We are leaders in using Big Data, Behavioural Science and AI/Machine Learning to understand human behaviour through the lens of mass datasets, across sectors ranging across health, retail, mobility, energy and communications. All N/LAB projects target social good, and push the boundaries of AI, in support of sustainable interventions and data-driven policy decision making. In partnership with citizens, business, and the public sector, we derive novel forms of demographic intelligence generated from digital footprint data streams, partnering with NGOs, governments and multinational companies across the globe.

Example Research Projects

Selected Recent Publications

Dimensions underlying public perceptions and misperceptions of food’s environmental impact.
Food systems are a major contributor to environmental impacts such as greenhouse gas emissions and biodiversity loss, with widespread dietary changes required to avoid surpassing safe planetary boundaries by 2050. To promote dietary shifts among the public it is crucial to… [more]

Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation.
Deprivation pushes people to choose cheap, calorie-dense foods instead of nutritious but expensive alternatives. Diseases, such as obesity, cardiovascular disease, and diabetes, resulting from these poor dietary choices place a significant burden on public health systems. Measuring nutritional insecurity is difficult to achieve at scale and… [more]

An empirical critique of the low income low energy efficiency approach to measuring fuel poverty.
Fuel poverty is a complex socioenvironmental issue of increasing global significance. In England, fuel poverty is assessed via the Low Income Low Energy Efficiency (LILEE) indicator, yet concerns exist regarding… [more]

Detecting iodine deficiency risks from dietary transitions using shopping data
Plant-based product replacements are gaining popularity. However, the long-term health implications remain poorly understood, and available methods, though accurate, are expensive and burdensome, impeding the study of sufficiently large cohorts. To identify dietary transitions over time, we examine anonymised loyalty-card shopping records from … [more]

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of non-prescription medications commonly bought for managing respiratory symptoms, to improve forecasting of weekly registered deaths from respiratory disease at local levels across England… [more]

Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey
Shopping data can be analyzed using machine learning techniques to study population health. It is unknown if the use of such methods can successfully investigate prediagnosis purchases linked to self-medication of symptoms of ovarian cancer… [more]

Pedagogical features of interactive apps for effective learning of foundational skills
Interactive apps are commonly used to support the acquisition of foundational skills. Yet little is known about how pedagogical features of such apps affect learning outcomes, attainment and motivation… [more]

Exploring young voter engagement and journey mapping across political events
This interdisciplinary study aims to explore the lived experiences and engagement of young voters from a customer journey perspective. To achieve this, the present study investigates voter engagement journey with various political events… [more]

Mapping the landscape of Consumer Food Waste
Since 2015 there has been a surge of academic publications and citations focused on consumer food waste. To introduce a special issue of Appetite focused on the drivers of consumer food waste we perform… [more]

Using mobile money data and call detail records to explore the risks of urban migration in Tanzania
Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on… [more]

Identifying food insecurity in food sharing networks via machine learning
Food insecurity in the UK has captured public attention. However, estimates of its prevalence are deeply contentious. The lack of precision on the volume of emergency food assistance currently provided to those in need is made even more ambiguous due to… [more]

The plexus of consumer analytics and decision-making
We develop the concept of exogenous cognition (ExC) as a specific manifestation of an external cognitive system. ExC describes the technological and algorithmic extension of (and annexation of) cognition in a consumption context. ExC provides a framework to enhance… [more]

Some of our Project Partners

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