Research
Health data science, real-world evidence, machine learning, and reproducible research software.
Research Interests
- Real-World Evidence, Real-World Data, and Observational Health Research
- Reproducible Health Data Science Workflows
- OMOP Common Data Model, OHDSI Tools, and Pharmacoepidemiology
- Federated Learning, Privacy-Preserving ML, and Efficient Healthcare AI
- Machine Learning for African Languages, Speech, and TTS
- Time-Series Modelling and Financial Markets
- Computer Vision for Geoscience and Healthcare Applications
- MLOps and Production ML Systems
Research Profiles & Software
ORCID
Research identifier and academic profile.
HDR UK Profile
Profile from my HDR UK Black Internship Programme work in health data science and federated healthcare research.
OmopStudyBuilder
Contributed to research software for building reproducible OMOP-based study workflows.
OmopViewer
Worked with OHDSI/OMOP tooling for visualising and exploring OMOP study outputs.
Projects & Collaborations
University of Oxford, NDORMS
Research Assistant in Health Data Science, contributing to projects involving real-world data, real-world evidence, observational health research, and reproducible health data workflows.
HDR UK & Newcastle University
Adaptive federated learning research for healthcare, focusing on privacy-preserving machine learning, Flower/Ray, model compression, and continual learning.
Alan Turing Institute & British Geological Survey
Geoscience computer vision research through the Alan Turing Institute Data Study Group, contributing to a final report with the British Geological Survey on applying machine learning to geological data analysis.
ToNative.org
Led development of ML-powered translation tools for English datasets into African languages including Yorùbá, Igbo, and Kinyarwanda.
See also: Projects