Selected work across laboratory information management, research data systems, project monitoring, traceability, dashboards, and AI-ready workflows.
The platform-level work I'm proudest of — each framed by why it matters and what it demonstrates.
TraceLab is a laboratory information management system designed to turn sample handling, lab workflows, approvals, and reporting into a traceable system of record.
Sample integrity is the backbone of trustworthy lab results — lose the chain and everything downstream is in doubt.
Shows I can model a regulated, audit-heavy domain and turn it into a dependable system of record.
ImpactBoard is a project monitoring system designed to turn scattered progress updates, deliverables, and documents into clear dashboards and stakeholder-ready views.
Programs are judged on progress, and progress stays invisible until updates, deliverables, and decisions are pulled into one legible view.
Shows I can turn fragmented progress reporting into a clear decision surface for non-technical stakeholders.
An experience-backed case study on research data workflows — from field data capture and validation to metadata, repositories, PostgreSQL, CKAN, FAIR principles, and AI-ready data foundations.
Agricultural research decisions ripple outward, so the data behind them has to hold up to scrutiny.
Shows I can operate inside real research workflows where data quality is non-negotiable.
The decisions that show up in every system, from the first schema to the last screen.
Map how people actually work before writing a schema, so the system fits the work instead of fighting it.
Scope access to roles and keep sensitive data behind clear, enforced boundaries.
Make every record explain where it came from, so results stay defensible over time.
Favour clear structure and readable code so a system outlasts the people who first built it.
Structure and describe data so it feeds dashboards, decisions, and models without rework.