At Infectious Diseases Research Collaboration (IDRC), I build Python ETL pipelines that move clinical trial data into Microsoft SQL Server and manage data operations for malaria and other health studies. I also use DHIS2 to retrieve clinical data for the PMC-VAC trial.
For the SMARTNET Machine Learning Study, I lead the end-to-end workflow for accelerometer-based behaviour classification, from sensor instrumentation and ETL to model validation and reporting. I trained and validated Random Forest classifiers, and built a locally hosted RAG system so investigators can access study findings without sensitive data leaving controlled infrastructure.
Across multi-site studies, I develop data-management web applications and maintain DIPPS, a Python participant follow-up system. I work with clinical, laboratory, sensor, image and audio data under strict confidentiality, ethics and data-protection requirements, and hold an MSc in Data Science and Analytics.
