Chris is an Enterprise Fellow as part of the IT Innovation Centre within the School of Electronics and Computer Science. He is also a Research Engineer with the Digital Health and Biomedical Engineering Research Group and is an honorary Research Engineer at University Hospitals Southampton Foundation Trust (NHS).
Chris is a CO-I on a number of large-scale interdisciplinary research programmes at the boundary of technology and society (e.g., healthcare, finance) and has significant experience in translating research into real-world impact in industry and the NHS. Chris’ main area of expertise lies in Artificial Intelligence and Machine Learning with an emphasis on model explanability (i.e., making machine learning interpretable by all).
Chris’ original background is Astrophysics (St Andrews & Flatiron Institute). More in the biography.
- Artificial Intelligence for Health and Wellbeing
- Explainable Machine Learning
- Human Centred Interactive Systems
- Chronic Disease Management
Chris' current research focusses on the following areas:
- Chronic Disease Management: This research involves the application of AI and machine learning to understand if it can help reduce the burden of managing a chronic condition. A variety of technology exists (self-management apps, sensing devices), however, engagement is critical for its success and the ability to train AI from this data. Ongoing projects tackling type-one diabetes and chronic obstructive pulmonary disease have directly included people with the conditions, family members, and clinical care teams to ensure needs are met.
- Anomaly Detection and Workforce Operations: This research conducted in the Financial Services sector with industry partner OnCorps involved detecting anomalies in transactional data, and fairly allocating the generated correction tasks across a multi-skilled workforce using AI. An end-to-end pipeline was developed to detect anomalies with machine learning and allocate tasks with a genetic algorithm.
- Publication(s) currently under review.
- Hospital Trajectories and Outcomes: This research area involves using machine learning techniques to develop decision support tools for clinicians. Utilising mixed data including Electronic Health Records and imaging, ongoing projects aim to identify patients at high risk and in-need of further clinical review.
External roles and responsibilities
Chris has significant experience in the application of statistical and machine learning techniques in Healthcare, Finance, and Astrophysics. Working within large interdisciplinary teams, he is a specialist in applied research and translating this into real-world impact. With a particular focus on Digital Health, Chris also tackles problems around acceptance and adoption of technology; including Artificial Intelligence (AI).
A key factor of acceptance is transparency and understanding. As a result Chris has significant experience in explainable AI (XAI), which aims to highlight the important factors in why a decision or prediction has been made by a model.
Chris is also involved in communicating AI to industry partners, clinicians, and the public. Working directly with psychologists and human-computer interaction experts, his work aims to include all stakeholders into the design process through co-design.