I began in a laboratory and ended up in the handoffs between a protocol and a household.

Background

My path into public health began in the laboratory. I trained in biotechnology and spent formative years in research environments where precision, reproducibility, and evidence were non-negotiable. That foundation gave me a lasting respect for rigorous methods, but it also raised questions that lab work alone could not answer. I wanted to understand how science translates into the lives of communities, who benefits, who is left out, and how systems can be designed to close those gaps.

I eventually stepped out of the lab and into public health systems. Today my work sits at the intersection of maternal and child health, mixed-methods research, and community-based participatory approaches. I am interested in how interventions are designed, delivered, and sustained, and in making sure the evidence we generate is both robust and usable by the people and institutions that need it.

I have led and contributed to studies that combine qualitative and quantitative methods, from formative fieldwork to impact evaluation. I care deeply about data integrity, ethical conduct, and the kind of field immersion that allows research to reflect reality rather than assumptions. I also follow developments in digital health and AI with curiosity and care, exploring where new tools can extend our understanding without displacing the relationships and judgment that make public health work trustworthy.

How I approach research

I approach research as a practice of careful attention. Participatory design is not a checkbox; it is a commitment to co-creating questions and methods with the communities and stakeholders who will live with the results. I prioritise field immersion and time on the ground because context shapes both the problem and the plausibility of any solution.

Data integrity and ethical rigour are non-negotiable. I design studies so that consent is meaningful, data are protected, and findings are reported with honesty about limitations. I also hold a systems awareness: health outcomes are rarely determined by a single intervention. I look for leverage points within policy, financing, workforce, and norms, and I aim to produce evidence that can inform decisions at those levels.

I am committed to health equity. That means asking who is included in research, who benefits from interventions, and how we can design and implement programs that reduce rather than reinforce disparity.

Where AI fits

I came to AI the way I came to public health: from the lab bench, with respect for what a well-built instrument can do and suspicion of what it cannot. Most of my advisory work is about scoping. Which handoff is actually failing? Would a redesigned form fix it? If a model is genuinely the right tool, where does the data live, who checks the outputs, and who absorbs the consequence when a flag is wrong? I write about these questions in the archive and describe the work itself on sheet 04.