Academic Background

I recently completed my PhD in Health Data Science at the University of Cambridge. My research bridged together the fields of machine learning and epidemiology, and my thesis was titled “Evaluating and enhancing cardiovascular disease risk prediction using algorithmic fairness”. I was part of the Health Data Research UK-Turing Wellcome PhD Programme, supervised by Angela Wood, Mike Inouye, and Sam Lambert. I am currently a Research Scientist at Helmholtz Munich in AI & Environmental Health, where I am using machine learning to better understand relationships between the environment and health outcomes, jointly considering algorithmic fairness.

Before my PhD, I was a DeepMind Scholar in MPhil Advanced Computer Science at the Cambridge Computer Laboratory, supervised by Neil Lawrence. I specialised in AI and ML courses, and my thesis topic was “Fairness and the bias-variance trade-off”. I was a member of Clare Hall College throughout my time in Cambridge.

I completed my first-class BSc in Computer Science at the University of Birmingham, specialising in AI, with visiting student placements at the University of British Columbia and the University of Waterloo.

After my undergraduate degree, I worked as an AI research consultant, where I joined a start-up company building artificial intelligence software for autonomous vehicles. Here, my research was on the implementation and optimisation of multi-objective route planning algorithms. For this work, I have two patents: Vehicle Route Guidance and Target Speed Optimisation.

I especially love the collaborative elements of research, learning from people from with different perspectives - feel free to get in touch for a chat via Bluesky, LinkedIn, or e-mail cic31[at]cam.ac.uk!

Publications & Patents

Cardiovascular risk prediction using metabolomic biomarkers and polygenic risk scores: A cohort study and modelling analyses
SC Ritchie, X Jiang, L Pennells, Y Xu, C Coffey, Y Liu, P Surendran, S Karthikeyan, SA Lambert, J Danesh, AS Butterworth, A Wood, S Kaptoge, E Di Angelantonio, M Inouye
medRxiv preprint (2023)
Axes of prognosis: Identifying subtypes of COVID-19 Outcomes
E Whitfield *, C Coffey *, H Zhang, T Shi, X Wu, Q Li, H Wu (* = equal contribution)
American Medical Informatics Association Annual Symposium conference paper, presentation recording (2021)
Achieving Net Zero within the NHS: System-wide transition to greener, sustainable care
University of Cambridge ThinkLab: B Eldridge-Thomas, S Carden, C Bücker, B Davies, S Ajadi, C Coffey, A De Fauconberg, A Hourihane, B Klein, W Moody, I Sami, A Santaro, A Sobey, C Tobin, C Tsai, V Viisainen, Y Zou
University of Cambridge Apollo, commissioned scientific report (2022)
Vehicle Route Guidance
JA Kelly, M Lathbury, E Bacci, C Coffey, AS Bahra, D Parker, NA Hawes
US patent (2019)
Target Speed Optimisation
JA Kelly, C Coffey, AS Bahra, M Lathbury, NA Hawes, D Parker
GB patent (2018)

Academic Talks

International Day of Women in Statistics and Data Science (IDWSDS) Conference (2024): Data Science for Health Equity: Algorithmic Fairness in Cardiovascular Disease Risk Prediction.
European Conference on Machine Learning (ECML PKDD) (2024): Understanding and Improving Algorithmic Fairness in Cardiovascular Disease Risk Prediction.
HDR UK Cambridge Community Meeting (2023): Understanding and Improving Fairness in Medical Risk Prediction.
HDR UK Doctoral Immersion Week on Fairness in healthcare data modelling(2023): Algorithmic fairness in cardiovascular disease risk prediction.
American Medical Informatics Association Annual Symposium (2021): Axes of prognosis: Identifying subtypes of COVID-19 Outcomes. Recording.
HDR UK Bimonthly Webinar (2021) : Running and completing a PhD in the time of COVID with Peter Diggle.
UCL MedTech Conference (2021): Sharing my experiences in MedTech.

Public Engagement & Science Communication

Cambridge Festival (2024): Public talk on Artificial Intelligence: With great power comes great responsibility. Abstract.
Cambridge Festival (2023): Public talk on Fairness in medical risk prediction algorithms. Recording.
Blog posts.

I am keen to be involved in more science communication work, as I believe public engagement should be a core part of research.

Teaching

Artificial Intelligence: Theory, Responsibility, and Sustainability (2023): Co-designed and delivered this course for high school students. My sections focused on on AI for sustainable development, climate change, and AI in the Global South.
MPhil Population Health Sciences (2023): Teaching assistant on the Advanced Statistics for Epidemiology module.
High school Computer Science (2017-2022): Personalised tutoring for GCSE and A-Level; teaching computer science in schools years 7-13.

Academic Activities

Statistical Methods for Health Equity Organiser (2021-2024): Data Science for Health Equity. Organising seminars, running a reading group, and conducting research on statistical methods for health equity. We run a yearly symposium on Statistical Methods for Health Equity.
Journal manuscript reviewer: For Science and European Heart Journal (collaborative lab reviews).

Other Projects

Reinforcement learning for healthcare: Explored various reinforcement learning paradigms with a focus on offline RL and how this can be applied to the problem of multimorbidity trajectory modelling. Supervised by Chris Yau.
Fairness and the bias-variance trade-off: MPhil thesis, exploring bias and variance errors in ML automated decision making in the context of algorithmic fairness, using data on criminal recidivism. Supervised by Neil Lawrence.
Multi objective journey optimisation: BSc thesis, created an AI journey optimisation system for autonomous vehicles with the University of Birmingham and Inventive Cogs. Supervised by Dave Parker.
Risk communication: Building predictive models to reduce health inequities also requires adequate communication and presentation of risk to patients. I am therefore involved a project on evaluating disease risk communication tools led by Owen Taylor.

Personal Interests

Singing is one of my favourite hobbies, and I am a member of Clare Hall Choir. Elsewhere, I have been a soloist, lead vocalist, backing vocalist, and chorister in genres ranging from rock to classical!

I am also passionate about sustainability, and in particular, local sustainability initiatives and growing food. I co-founded the University of Cambridge Allotment Society, where we built a community to grow fruit and vegetables sustainably, and donate excess to local charities. This included writing grant applications for funding, managing the day-to-day running of the allotment, and social media management.

I love learning new things, meeting new people, and keeping active; I have taken up various hobbies to feed this curiosity. I adore travelling, especially exploring nature! My other hobbies include hiking, dancing (ex-UK freestyle display team), yoga, digital art, and photography. I also train in Fujian White Crane Kung Fu and rowed in Clare Hall Boat Club, where I was social secretary and organised the inaugural international training camp for the club.

Claire Coffey

PhD in Health Data Science, Cambridge University
Algorithmic fairness | clinical prediction models | AI for health