MINDSET brings together researchers, trainees, NHS and public-sector data partners, treatment services, and collaborating academic groups around a shared aim: understanding how mental health develops by drawing credible, useful, and responsible inferences from heterogeneous evidence across levels, settings, and timescales.

Different projects draw on combinations of epidemiology, data science, lived-experience expertise, clinical and service knowledge, population and environmental data, intensive longitudinal measurement, biological science, and research infrastructure. The people and organisations below reflect active or longstanding relationships; no single project or partner is expected to cover every part of the programme.

Research team

Headshot of Justin C Yang
Dr Justin C Yang
Senior Research Fellow · MINDSET Lead
Psychiatric epidemiology · multimodal data · causal inference
UCL Profile
Headshot of Nathalie Rich
Dr Nathalie Rich
Research Fellow
Electronic health records · psychiatric epidemiology
UCL Profile
Headshot of Yunsoo Kim
Dr Yunsoo Kim
Research Fellow
Natural language processing · large language models
Website

Trainees

Headshot of Vanessa Cieplinska
Vanessa Cieplinska
PhD Student
School attendance and educational attainment in children with neurodevelopmental conditions
Justin contributes as secondary supervisor
Headshot of Alua Yeskendir
Alua Yeskendir
PhD Student
Health and social inequalities in psychiatric disorders
Justin contributes as secondary supervisor

I also contribute to doctoral supervision, thesis committees, and wider trainee development. See Teaching & Supervision for the broader training programme.

The MINDSET research ecosystem

Many partners contribute across more than one area of the programme.

MINDSET Partners and expertise across four connected areas
Population, place & linked data

Following mental health across systems and populations

National administrative, longitudinal, environmental, and spatial data; population data science; linkage; and infrastructure for studying trajectories and inequalities at scale.

Clinical data & infrastructure

Turning routinely collected records into research evidence

Electronic health records, clinical text, governed data access, harmonisation, and methods for research across mental-health services.

Experience, biology & methods

Connecting mechanisms, context, and measurement

Intensive longitudinal and lived-experience measures, social and environmental context, biomarkers and immunology, neuroimaging, causal inference, systems thinking, prediction, and inequalities.

Services, policy & lived and living experience

Keeping research connected to lived and living experience, care, and decision-making

Lived- and living-experience expertise, treatment-provider expertise, public involvement, service context, and partnerships that help make questions and interpretation useful beyond academia.