Dr Cedric E. Ginestet is a Lecturer in Biostatistics in the Department of Biostatistics & Health Informatics at King’s College London, where he has worked since 2009 in a range of research, postdoctoral and lecturing roles.

His research focuses on the development and application of statistical methods for complex biomedical and psychiatric data, including Bayesian modelling, causal inference, and network analysis of neuroimaging and mental health data. He has collaborated on numerous interdisciplinary studies in psychiatry and neuroscience, contributing statistical expertise to large-scale projects investigating conditions such as schizophrenia, autism, and Huntington’s disease.

Dr Ginestet previously held a postdoctoral position in the Department of Mathematics and Statistics at Boston University, where he worked on statistical methods for network data with applications to neuroscience.

He holds a PhD in Biostatistics from Imperial College London, as well as an MPhil and BSc in Psychology from the University of West London, and a degree in Social Sciences from the University of Toulouse.

He has published widely in leading journals in statistics, psychiatry and neuroimaging, contributed to a book chapter on network analysis, and received several academic awards, including the American Statistical Association’s Postgraduate Paper Prize for Excellence in Bayesian Methodology.

Research interests

Methodology

• Network analysis
• Causal analysis
• Bayesian statistics
• Information geometry

Applications

• Neuroimaging
• Clinical trials
• Spatial epidemiology

Teaching

MSc programme Applied Statistical Modelling and Health Informatics (ASMHI)

• 7PAVITPR: Introduction to Statistical Programming.
• 7PAVITSM: Introduction to Statistical Modelling.
• 7PAVPRMD: Prediction Modelling.
• 7PAVCIAE: Causal Modelling and Evaluation.
• 7PAVREPR: Research Report (MSc Dissertation).

MSc in Organizational Psychiatry and Psychology (OPP)

• 7PCSORME: Introduction to Applied Statistical Methods.

Doctorate in Clinical Psychology (DClinPsy)

• RAM: Research and Methodogy (Statistical components).

Expertise and public engagement 

Radical Statistics Group: Using statistics to support progressive social change. Presented a paper on Continuous Voting Systems at the 2026 Radical Statistics conference.