Claire Coffey, PhD

Co-founder of NOA Health · Visiting Researcher, University of Cambridge

Making personalised, preventive care a reality by combining rigorous health data science with technology people can actually use.

Get in touch

Portrait

What I do

01

NOA Health

Co-founding and building an AI-powered preventive health platform that integrates blood biomarkers, lifestyle data, and clinical insight.

02

Responsible AI in healthcare

Designing fair, interpretable ML systems that work equitably across different populations.

03

Research to product

Translating rigorous health data science into tools that clinicians and individuals 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 the technical co-founder and CTO of NOA Health, an AI-powered preventive health platform that brings together blood biomarkers, wearable data, and lifestyle information to identify patterns that would otherwise go unnoticed. I currently also 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.

Email me

Selected highlights

Pitch prize · ai@cam Sciencepreneurship (2026)

Won a pitch prize for a preventive health AI concept focused on making prevention more personalised and scalable.

Info

Paper · European Heart Journal (2025)

Co-author on work combining clinical, metabolomic, and polygenic scores for cardiovascular risk prediction.

Read

More

Recent milestones and updates.

  • NOA Health announces out of stealth.
    Jun 2026 Announcement
  • NOA Health selected for Cambridge Spark accelerator.
    Jun 2026
  • NOA Health awarded BW Pre-Seed funding and officially founded.
    Apr 2026
  • Paper published: "Prediction of allergic disease trajectories from birth up to adolescence" (Pediatric Allergy and Immunology).
    Apr 2026 Paper
  • 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).
    Dec 2025 Event Watch
  • 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 Update
  • 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.

  • Prediction of allergic disease trajectories from birth up to adolescence.
    Pediatric Allergy and Immunology · 2026 Paper
  • 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.
    AMIA Annual Symposium · 2021 Paper Recording
  • Achieving Net Zero within the NHS: System-wide transition to greener, sustainable care.
    Commissioned report (Cambridge ThinkLab / Apollo) · 2022 ThinkLab Report
  • Vehicle Route Guidance.
    US patent · 2019 Patent Google Patents
  • Target Speed Optimisation.
    GB patent · 2018 Patent IPO

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.
    2021 AMIA Recording
  • 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.

  • 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).
    2021–2024 DS4HE 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

Get in touch

Open to selected collaborations, speaking, media, and advisory conversations in preventive health, responsible AI, and health data science.

Email me LinkedIn
Google Scholar GitHub Bluesky Medium

clairecoffey@live.co.uk