Summary
Amy joined the MRC Biostatistics Unit in March 2026, as a member of the Burgess group led by Dr Stephen Burgess. She previously worked with the group as a member of the Cardiovascular Epidemiology Unit. Her research focuses on Mendelian randomisation (MR) methods and their applications, combining methodological development with applied analyses in cardiovascular disease and other complex traits. She has a particular interest in non-linear relationships and heterogeneous ancestry populations.
Prior to this, Amy completed her PhD in Random Matrix Theory at the University of Bristol. She retrained in statistics via a Graduate Diploma in Statistics from the Royal Statistical Society and worked as a statistician at the Modernising Medical Microbiology group at the University of Oxford before moving to Cambridge in 2017. Her previous research included using large, linked NHS datasets to investigate weekend mortality in Oxfordshire, comparing programs for analysing antibiotic resistance in S. aureus bacteria, and modelling nasal presence of variants of S. aureus.
Beyond research, she is heavily involved with the department's public engagement and greatly enjoys meeting members of the public to discuss the implications of research. In her spare time, she enjoys making elaborate embroidery and crochet, or dressing up as a fictional character at Empire LRP.
Research
Amy’s research spans both methodological development and applied analyses. Amy's development work looks at extending existing Mendelian Randomisation methods such as where the underlying causal relationships are non-linear or investigating best practise when methods are being applied across heterogeneous subpopulations of different sizes. She is also involved in the development of statistical software, including R packages to facilitate reproducible MR analyses.
On the applied side, she works extensively with large-scale biomedical datasets such as UK Biobank and All of US. This includes curating and harmonising outcome data from multiple sources, generating genetic association datasets, and interpreting causal estimates from MR studies. Her applied work has focused particularly on cardiovascular diseases, as well as considering how differences in data definitions can affect epidemiological conclusions. She maintains an active interest in improving data quality and representativeness in large health records.
A list of her publications is available from ORCID and Google scholar; her code can be found at GitHub.
Teaching
Amy teaches methods and R code in the Mendelian Randomisation course and supports lecturers in the Cambridge's MPhil in Population Health Sciences.
Amy has supported several master students and interns.
Clare Hole (MPhil, Disentangling Causality between OSA and adiposity with CVDs using Mendelian Randomization)
Clara Zettelmeyer (MPhil, Validation of a Chronic Kidney Disease Risk Prediction Model using the UK Biobank Cohort)
Summer interns: Jim Broadbent, Ifeanyi Chukwu
Public Engagement
Amy regularly gives talks on statistical topics and runs creative activities for public events and school groups. She uses art, gaming and crafting approaches to engage people in statistical thinking. She is currently developing a course on drawing causal diagrams for KS2 pupils.
She ran Pathogen, a collaborative game about viral epidemics, for the Cambridge Festival in 2018 and 2020, and Genetic Risk Arts and Crafts in 2023 and 2024.
If you would like her to give a talk or organise an activity for your school or group, either in person or online, please contact her by email.
Disability Staff Network
Amy is on the committee for university's Disability Staff Network. If you are a disabled member of the department (or university!) who needs support in requesting reasonable accommodations or navigating UK disability support, please get in touch by email.
