Academic Research

Engaging academics from different disciplines (e.g. business, computer science, medicine) to develop proposals, and conduct multidisciplinary research, on the impact of AI on different sectors (e.g. finance, health), and to present and publish completed research at world leading academic conferences and journals

Selective Research Projects
Developing a Heuristic Tool Set for AI Implementation in Incumbent Firms

The implementation of Artificial Intelligence (AI) brings at least two challenges for the incumbent firm. It involves a) determining which tasks in an already established business to target; and b) identifying AI technologies that correspond to these value creation opportunities. However, there is limited research that can guide incumbent firms through implementation of AI.  Extant research tends to be technical, or uncritically presenting an idealized future, largely detached from the business reality of incumbent firms.  To address these challenges, we develop a “heuristic tool” set for AI implementation in incumbent firms by embarking on an Action Design Research (ADR) project, designed to contain two ADR cycles, in collaboration with a long-established, multinational food processing and retailing company, AIXCo. In this short paper, we document our research-in-progress and its ambition to contribute to the emergent literature knowledge in information systems on AI in general and AI implementation in particular. The proposed heuristic tool views AI implementation as recombination of humans and machines capacities, helping firms to break down their current business tasks, to identify where it generates value, and to plan a feasible change.   

Democratising FX Expertise

This project aligns leading FX, ML and research expertise to facilitate the development of a world-first, machine learning enhanced FX exchange, based on The Money Cloud (TMC's) award-winning platform, enabling users from all over the world to easily establish the best FX exchange options and predict market dynamics to an unheralded level of accuracy. TMC will harness users' transactional data and use machine learning to provide analysis of past transactions and recent behaviour well as predictive forecasts for trading clients or to sell to relevant organisations.

Stay Happy: Understanding Urban Wellbeing Using a Behavioural Machine Learning Approach

This research will address urban wellbeing through: an innovative approach to data analytics, Behavioural Machine Learning (BML); the creation of new urban policies as a result of improved personalisation; and the interaction with data (including self-generated data by citizens as well as citizen data by businesses and policymakers) in day-to-day decision-making. The proposed research involves large datasets collected from field experiments as well as publicly available data, the former particularly focused on the complex and large datasets that capture individual citizen characteristics and wellbeing. Our research is innovative because it: (a) broadens and integrates research in behavioural science, data analytics, computer science, and human-data interaction (HDI); (b) examines decision design for complex data-driven urban decisions that involve datasets with low informativeness, very large datasets which are difficult to manage, and noisy data; and (c) focuses on how the data is used by citizens, businesses, and policymakers. It will have a broad impact in several ways. Firstly, we will work directly with multiple stakeholders to generate solutions that have practical implications for creating new urban policies in practice with measurable benefits. Secondly, its research outputs will suggest improved participatory modes for analysis and presentation of different types of data in the digital economy more generally. Its third value is methodological, as we offer a model of close collaboration and integration for social and natural sciences research. As a result of this project, qualitative and quantitative researchers will better understand the limits and possibilities of the other's methodology, leading to better and more applicable interdisciplinary research. A final impact is on general education in science: blending social and natural science enriches and motivates students of all ages, as well as the members of the general public.

Applying AI-based solutions into high-value litigation

Litigation analytics is now increasingly seen as critical to the evolution of the litigation sector in England and Wales. In partnership with  Solomonic and funded by Innovate UK, this research aims to develop machine learning capabilities which will drive world-leading litigation data and analytics.

Entrepreneurship & Emotions 

Using Face++, a leading facial recognition technology, we examine whether entrepreneurship influences an entrepreneur’s emotions using a sample of 30 million Twitter messages sent by entrepreneurs and non-entrepreneurs across three different studies: London, Los Angeles, and worldwide.  Our results show that entrepreneurs experience more positive emotions in relation to all topics except business matters, towards which they experience less positive emotions.  We also find that, relative to other entrepreneurs, social entrepreneurs experience more positive emotions, while serial entrepreneurs experience less positive emotions.

Addressing Cybersecurity and Cybercrime via a co-Evolutionary aPproach to reducing human-related risks

Researchers and practitioners have acknowledged human-related risks among the most important factors in cybersecurity, e.g. an IBM report (2014) shows that over 95% of security incidents involved "human errors". Responses to human-related cyber risks remain undermined by a conceptual problem: the mindset associated with the term 'cyber'-crime which has persuaded us that that crimes with a cyber-dimension occur purely within a (non-physical) 'cyber' space, and that these constitute wholly new forms of offending, divorced from the human/social components of traditional (physical) crime landscapes. The project's overall aim is to develop a framework through which we can analyse the behavioural co-evolution of cybersecurity/cybercrime ecosystems and effectively influence behaviours of a range of actors in the ecosystems in order to reduce human-related risks. To achieve the project's overall aim, this research will (1) Be theory-informed: Incorporate theoretical concepts from social, evolutionary and behavioural sciences which provide insights into the co-evolutionary aspect of cybersecurity/cybercrime ecosystems. (2) Be evidence-based: Draw on extensive real-world data from different sources on behaviours of individuals and organisations within cybersecurity/cybercrime ecosystems. (3) Be user-centric: Develop a framework that can provide practical guidance to system designers on how to engage individual end users and organisations for reducing human-related cyber risks. (4) Be real world-facing: Conduct user studies in real-world use cases to validate the framework’s effectiveness. The new framework and solutions it identifies will contribute towards enhanced safety online for many different kinds of users, whether these are from government, industry, the research community or the general public.