Pitch prize · ai@cam Sciencepreneurship (2026)
Won a pitch prize for a preventive health AI concept focused on making prevention more personalised and scalable.
InfoClaire Coffey, PhD
Founder in preventive health AI · Building responsible, personalised prevention
I build AI-driven preventive health tools that turn complex health data into actionable, trustworthy insights for real-world use.
What I do
01
Responsible AI for preventive health
Trustworthy, interpretable, and fair AI systems designed for real-world health use.
02
Personalised prevention
Turning biomarker, wearable, and health data into more actionable and individualised prevention.
03
Research translation into product
Bridging rigorous health data science with practical tools that people and providers can actually use.
About me
I am a founder, researcher, and AI scientist working at the intersection of responsible AI, clinical data science, and preventive health. My work focuses on how health technologies can move beyond one-size-fits-all approaches to deliver more actionable, personalised, and trustworthy prevention. I am particularly interested in translating advances in machine learning and health data into tools that have real-world value for individuals and healthcare.
I currently hold a Visiting Researcher position at the University of Cambridge. I completed my PhD in Health Data Science at Cambridge, supported by Health Data Research UK, The Alan Turing Institute, and the Wellcome Trust, where my research focused on fairness in clinical prediction algorithms and polygenic risk scores.
Following this, I worked as a research scientist at Helmholtz Munich, developing explainable machine learning methods and reproducible modelling pipelines for environmental health using population-scale data.
Earlier in my career, I worked in AI R&D at a startup designing optimisation algorithms for autonomous vehicles and contributing to patented work. I also hold an MPhil in Advanced Computer Science from the University of Cambridge, where I was a DeepMind Scholar, and a BSc in Computer Science from the University of Birmingham.
Selected highlights
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Recent milestones and updates.
- Won a pitch prize at ai@cam Sciencepreneurship for a preventive health pitch (making prevention more personalised and scalable).Feb 2026 ai@cam
- Delivered seminar: “Are polygenic risk scores fair for cardiovascular disease risk prediction?” (Data Science for Health Equity).
- Paper published: “Combined clinical, metabolomic, and polygenic scores for cardiovascular risk prediction” (European Heart Journal).Dec 2025 Paper
- PhD graduation ceremony (University of Cambridge).Oct 2025 Update
- Preprint released: “Current polygenic risk scores are unlikely to exacerbate unfairness in cardiovascular disease risk prediction”.Sep 2025 Preprint
- Submitted final hardbound PhD thesis copy (thesis available online).Aug 2025 Thesis
- Presented poster: “AI-driven environmental epidemiology: a generalisable framework to predict cardiovascular health using fair and explainable machine learning” (Helmholtz AI).Jun 2025 Poster
- Passed PhD viva with minor corrections.Mar 2025
- Joined Helmholtz Munich as a Research Scientist (AI & Environmental Health).Feb 2025 Helmholtz
- Submitted PhD thesis: “Evaluating and enhancing cardiovascular disease risk prediction using algorithmic fairness”.Dec 2024
- PPIE report released: “Equitable, trustworthy and safe research in healthcare technology, data and artificial intelligence: an international dialogue”.Dec 2024 Report
- Poster accepted for AMLD EPFL 2025: Algorithmic fairness in CVD risk prediction models.Dec 2024 AMLD
Selected papers, preprints, reports, and patents.
- Current polygenic risk scores are unlikely to exacerbate unfairness in cardiovascular disease risk prediction.medRxiv preprint · 2025 Preprint
- Combined clinical, metabolomic, and polygenic scores for cardiovascular risk prediction.European Heart Journal · 2025 Paper
- Axes of prognosis: Identifying subtypes of COVID-19 Outcomes.
- Achieving Net Zero within the NHS: System-wide transition to greener, sustainable care.
- Vehicle Route Guidance.US patent · 2019 Patent Google Patents
- Target Speed Optimisation.
Invited seminars and conference talks.
- Data Science for Health Equity Seminar: Are polygenic risk scores fair for cardiovascular disease risk prediction?2025 Watch
- Helmholtz Munich Seminar: Improving Cardiovascular Disease Prediction: From Fair Algorithms to Environmental Insights.2025 Helmholtz
- International Day of Women in Statistics and Data Science (IDWSDS) Conference: Data Science for Health Equity: Algorithmic Fairness in Cardiovascular Disease Risk Prediction.2024 IDWSDS
- ECML PKDD: Understanding and Improving Algorithmic Fairness in Cardiovascular Disease Risk Prediction.2024 ECML PKDD
- HDR UK Cambridge Community Meeting: Understanding and Improving Fairness in Medical Risk Prediction.2023 HDR UK
- HDR UK Doctoral Immersion Week on Fairness in healthcare data modelling: Algorithmic fairness in cardiovascular disease risk prediction.2023 Course
- AMIA Annual Symposium: Axes of prognosis: Identifying subtypes of COVID-19 Outcomes.
- HDR UK Bimonthly Webinar: Running and completing a PhD in the time of COVID (with Peter Diggle).2021 Webinar Peter Diggle
- UCL MedTech Conference: Sharing my experiences in MedTech.2021 UCL MedTech
I’m keen to do more science communication work as I believe public engagement is an essential part useful research.
- Cambridge Festival: Artificial Intelligence — With great power comes great responsibility.
- Cambridge Festival: Fairness in medical risk prediction algorithms.
- Blog posts.
- Artificial Intelligence: Theory, Responsibility, and Sustainability (co-designed and delivered).2023
- MPhil Population Health Sciences — Teaching assistant (Advanced Statistics for Epidemiology).2023
- High school Computer Science — GCSE/A-Level tutoring and in-school teaching (years 7–13).2017–2022
- Statistical Methods for Health Equity organiser (seminars, reading group, research; yearly symposium).
- Journal manuscript reviewer (collaborative lab reviews).Science · European Heart Journal
- Reinforcement learning for healthcare (offline RL; multimorbidity trajectory modelling).Supervised by Chris Yau Chris Yau
- Fairness and the bias–variance trade-off (MPhil thesis; criminal recidivism data).Supervised by Neil Lawrence Neil Lawrence
- Multi-objective journey optimisation (BSc thesis; autonomous vehicles).Supervised by Dave Parker Dave Parker
- Risk communication: evaluating tools for presenting disease risk to patients.Project led by Owen Taylor Project Owen Taylor