Rate My Professor Marc Chadeau-Hyam

Marc Chadeau-Hyam is Professor of Computational Epidemiology and Biostatistics in the School of Public Health, Faculty of Medicine at Imperial College London. Since 2018, he has served as the programme director of the MSc in Health Data Analytics and Machine Learning, in collaboration with the Data Science Institute. His previous roles at the institution include Senior Lecturer in Biostatistics and Senior Lecturer in Statistical Bioinformatics. He holds an honorary position as Reader at Utrecht University. Chadeau-Hyam's research as an applied statistician focuses on the interface between mathematical and epidemiological sciences, developing novel statistical approaches to address biologically and epidemiologically driven questions. Key areas encompass computationally efficient models for profiling from high-throughput platforms, dynamic models for disease progression, data analysis and integration, exposome analytics, environmental epidemiology, and omics data integration. He contributes to research groups including the Data Analysis and Integration theme at the Mohn Centre for Child Health and the Life-course Epidemiology and Exposome group in the Department of Epidemiology and Biostatistics.

During the COVID-19 pandemic, Professor Chadeau-Hyam was a core member of the REACT study team, contributing to real-time national surveillance of SARS-CoV-2 community transmission, infection hospitalization and fatality ratios, antibody testing outcomes, persistent symptoms, and vaccine hesitancy and uptake analyses. His scholarly impact is substantial, with over 15,700 citations on Google Scholar across topics in statistics, epidemiology, and related fields. Key publications include 'State-of-the-art methods for exposure-health studies' (Environment International, 2022), 'A multivariate approach to investigate the combined biological effects of multiple environmental exposures: a pilot application in Italian women' (Journal of Epidemiology and Community Health, 2018), 'SARS-CoV-2 rapid antibody test results and subsequent risk of infection' (Nature Communications, 2023), 'Trajectory-Based Clustering to Identify Asthma Subgroups in a Prospective Birth Cohort' (2025), and 'Cognition and Memory Following COVID-19 in a Large Community Sample' (2023).