Simon N. Wood is a distinguished academic and leading authority in computational statistics with a career spanning over three decades. He currently serves as Chair of Computational Statistics in the School of Mathematics at the University of Edinburgh, a position he has held since 2020. His academic journey began with a BSc in Physics from the University of Manchester, followed by a PhD from the University of Strathclyde where he investigated estimation of mortality rates in stage-structured zooplankton populations. After completing his doctoral studies, he established his research credentials through a postdoctoral position at the NERC Centre for Population Biology at Imperial College London, before progressing through academic ranks at the Universities of St Andrews, Glasgow, and Bath.
Professor Wood's research has profoundly influenced the field of statistical modeling through his development of innovative smoothing techniques and generalized additive models, which have become standard tools in ecological and epidemiological research. His creation of the widely-used mgcv package in R has empowered researchers worldwide to implement complex statistical models with greater efficiency and accuracy. With over 60,000 citations to his name, his methodological contributions have proven particularly valuable in addressing complex real-world problems, including his critical analyses of statistical approaches during the COVID-19 pandemic. His work on estimating infection prevalence rather than relying solely on case counts provided essential insights that challenged prevailing pandemic response strategies.
Beyond his technical contributions, Professor Wood has significantly shaped statistical practice through his editorial leadership as co-editor of the Royal Statistical Society's Series B journal from 2018 to 2021. He has been a vocal advocate for rigorous statistical methodology and transparent communication of uncertainty, frequently challenging statistical misapplications in high-stakes decision-making contexts. Currently leading research projects extending through 2026, he continues to advance methodologies for complex data analysis while mentoring the next generation of statistical scientists. His ongoing commitment to statistical integrity and methodological innovation ensures his enduring influence on both theoretical and applied aspects of modern statistics.
