PhD Researcher, Health Data Scientist & Early Stage Investor
Translating complex health data into actionable insights. Specialising in AI-driven prediction models, machine learning, and digital health interventions to improve patient outcomes.
01 — About
PhD candidate at Imperial College London, funded by the NIHR, with a strong interdisciplinary background spanning both academia and industry. I specialise in applying advanced statistical and machine learning methods to real-world hospital data across multiple disease areas.
A former healthcare and life sciences consultant at EY, I now lead large-scale data analyses using UK Biobank, CPRD, and WSIC datasets. My research focuses on gestational diabetes mellitus, maternal health outcomes, and digital health interventions.
I possess technical fluency in R, Python, and SQL, combined with strategic insight from high-impact NHS and life sciences projects. Passionate about translating complex data into actionable insights that improve patient outcomes and accelerate digital transformation in healthcare.
02 — Experience
Proximo Ventures, London
Driving early-stage investments in pre-seed/seed healthcare startups from Imperial, Cambridge, and Oxford.
BMJ Quality & Safety, London
Curating and scheduling science communication content to enhance the journal's international visibility across X, LinkedIn, and Facebook.
Institute of Global Health Innovation, Imperial College London
Collaborating on studies across digital health, AMR, and women's health projects. Leading quantitative analysis of North West London EHR using WSIC datasets.
MSk Lab, Imperial College London
Led end-to-end linkage of UK Biobank and wearables datasets. Analysed CPRD (60M patients; 500GB). Co-authored 10+ studies leading statistical methodologies.
EY Parthenon, London
Led opportunity sizing for NHS One Digital Citizen (£3.5M business case). Reviewed $7B pharmaceutical portfolio forecasts. Developed MCDA framework for NHS Digital Channels.
Division of Surgery & Interventional Science, UCL
Leading all data analyses including NLP sentiment analysis. Co-author of 10+ peer-reviewed publications. Contributed to development of AesthetiSim metaverse platform.
EY Parthenon, London
Only consultant in the history of the team to promote early. \n Applying data science, machine learning, and healthcare sector expertise to deliver insights and solutions across a diverse portfolio of client-facing projects. Opened a new revenue stream for a health exchange information entity in the MENA region due to a research piece linking population health and dental data. Supported ICBs in their Elective Recovery Programme, building capacity & demand models, KPI dashboards, and demand forecasting.
King's Health Partners, King's College London
Principal bioinformatician of the CEDiD study - prediction of COVID-19 infection using pervasive monitoring. Analysed 100GB wearable data. Co-authored 5 published papers.
Virtuoso (start-up)
Developed the prototype of the mobile application using Flutter of Virtuoso, a MedEd / EdTech start up. Virtuoso entered demo day of London's unis (Imperial, UCL, KCL) and was powered by King's Accelerator andraised grant funding from Centre for Innovation & Development, Google Startups, and Amazon Startups
03 — Education
Imperial College London
March 2025 — Present
Title: A Data-Driven and AI-Enabled Evaluation of Remote Care in Women with Gestational Diabetes Mellitus
Supervisors: Professor the Lord Ara Darzi, Dr Ana Luísa Neves
Funding: Full PhD Studentship by NIHR Patient Safety Research Collaboration (£120,000)
Imperial College London
2019 — 2020
Research projects in cancer biology, programming, statistics, and bioinformatics.
• ANO6: A Potential Novel Prognostic Biomarker for Breast Cancer
• LASSO risk model for ovarian cancer prognosis
• Epigenetic exposure signatures in ovarian cancer
University of Warwick
2016 — 2019
Notable modules: Differential Equations (83%), Linear Algebra (80%), MATLAB Programming (90%), Introduction to Systems Biology (93%), Stochastic Processes (80%)
04 — Skills
05 — Publications
Unsupervised Machine Learning Models Reveal Two Distinct Post-Operative Physical Activity Profiles Among Joint Arthroplasty Patients: A United Kingdom Biobank Cohort Study
Arthroplasty, September 2025 — Editor's Choice Publication
Free Tissue Transfer for the Management of Diabetic Lower Limb Ulcers: A Systematic Review and Meta-Analysis
Microsurgery, July 2025
COVID-19 Early Detection in Doctors and Healthcare Workers (CEDiD) study: a cohort study on the feasibility of wearable devices
BMJ Open, 2025
A comparison of acellular dermal matrices (ADM) efficacy and complication profile in women undergoing implant-based breast reconstruction: a systematic review and network meta-analysis
BMC Cancer, 2024
A systematic review of Generative Adversarial Networks (GANs) in Plastic Surgery
JPRAS, April 2024
06 — Recognition
2025/26
2025-2028 • £120k
2023 • Royal College of Surgeons
2022 • PLASTA x Royal Free Hackathon