Kimberley Kavanagh

Professor Kimberley Kavanagh is a leading applied statistician at the University of Strathclyde, affiliated with the Department of Mathematics and Statistics within the Faculty of Science. She collaborates extensively with NHS clinicians, epidemiologists, and international partners to develop data-driven solutions for public health, medicine, and social care. Her work has directly influenced public health policy, particularly in HPV vaccination and cervical screening in Scotland and globally.

Her research interests are centered on applied statistics, observational data analysis, data linkage, and prediction modeling to extract maximum value from health datasets. She applies robust statistical methods to real-world problems in epidemiology, maternal health, cancer outcomes, and vaccine effectiveness. Her expertise bridges statistical science and clinical application, particularly in public health decision-making.

The recent trend in her publications shows a strong focus on AI-informed clinical risk assessment, especially in maternal health (e.g., pre-eclampsia), and evaluation of real-world vaccine impact. Her work increasingly integrates machine learning with traditional epidemiological methods, contributing to predictive tools for adverse outcomes in pregnancy and long-term cancer surveillance.

Scientific Awards:

• Royal Society of Edinburgh 2018 Sir Thomas Makdougall Brisbane Medal
• Prize-winning presenter at Young Statisticians Meeting 2010

Professor Kavanagh is actively involved in major research grants and advising roles. She serves as a Principal Investigator and Co-investigator on projects such as the Cancer Medicines Outcomes Programme and panPIERS, focusing on AI-based risk assessment in pre-eclampsia. She advises the Scottish Government and the Royal Statistical Society, contributing her statistical expertise to national health policy. Her work is supported by collaborations across Public Health Scotland, NHS, and international institutions.

She leads and participates in interdisciplinary research teams, particularly in projects involving AI in healthcare, cancer surveillance, and maternal risk modeling. Her lab-like collaborative environment includes statisticians, clinicians, and data scientists working on predictive analytics for public health impact.