معرفی
Professor Angela Wood serves as Professor of Biostatistics and Health Data Science in the Department of Public Health and Primary Care at the University of Cambridge, with her research centered in the Cardiovascular Epidemiology Unit. She also holds the prestigious position of Vice-Master at Darwin College where she serves as College Tutor.
Dr. Wood earned her BSc (Hons) in Mathematics and Statistics in 1998 and completed her doctorate in 2001 under the supervision of Profs Peter Diggle and Robin Henderson at the University of Lancaster. Following post-doctoral research with Prof Ian White at the MRC Biostatistics Unit in Cambridge, she joined the University of Cambridge in 2006 as a University Lecturer in Biostatistics.
Her research interests focus on advancing statistical methods for epidemiological research, particularly in cardiovascular disease. Professor Wood specializes in health data science applications for complex disease prediction, with expertise in large-scale data integration and analysis. Her work bridges biostatistical methodology development with practical applications in population health.
Professor Wood holds significant leadership positions including co-Lead of the Big Data for Complex Disease Driver Programme for HDR UK; BHF Data Science Centre Associate Director and Theme Lead for Structured Data; co-Lead of the NIHR Cambridge BRC Data Science and Population Health theme; Regional co-Lead for Health Data Research UK Cambridge; Programme Leader in the NIHR Blood and Transplant Research Unit; Turing Fellow at the Alan Turing Institute; and Steering Group Member of the European Society of Cardiology Cardiovascular Risk Collaboration Unit.
As an active supervisor, Professor Wood takes PhD students through the Department's doctoral training program in Public Health and Primary Care. Her leadership extends to major national health data initiatives that shape research directions in cardiovascular epidemiology and population health sciences. She contributes to the Cardiovascular Epidemiology Unit's work in health data science, molecular epidemiology, and systems genomics, with particular emphasis on methodological development and translational research.
