Biography
I am a Research Fellow at the University of Cambridge, affiliated with the Cardiovascular Epidemiology Unit and the Centre for Cancer Genetic Epidemiology (Department of Public Health and Primary Care), as well as the Cambridge Centre for AI in Medicine (Department of Applied Mathematics and Theoretical Physics). My research develops statistically and computationally efficient methods to unlock the potential of whole-population health datasets. I also build statistical and machine learning models that leverage multimodal data to enhance risk prediction, especially for the early detection of cancer and cardiovascular diseases. My aim is to translate complex big data into clinically relevant tools and insights that support healthcare professionals and inform policy decisions. With a background in mathematics (MMath, University of Cambridge) and theoretical physics (PhD, Max-Planck-Institut für Astrophysik), I integrate cross-disciplinary insights to tackle multifaceted challenges in healthcare.
Research
Sam now channels her quantitative skills into healthcare research. She finds immense joy in linking concepts across diverse disciplines, from biostatistics to economics, computer science, physics, and more, using insights from various fields to illuminate and address gaps in healthcare data science.
Presently affiliated with the Cardiovascular Epidemiology Unit, Centre for Cancer Genetic Epidemiology, and soon also the MRC Biostatistics Unit, Sam’s research spans:
- Analysing national electronic health records to understand COVID-19’s impact
- Developing new methods for early cancer detection
- Enhancing clinical decision support systems by effectively quantifying and managing uncertainties
Above all, Sam’s aspiration is to make a tangible difference in people’s lives through her research.
Publications
For a list of Sam’s publications, visit her ORCID profile.
