About me

I work at the intersection of welfare measurement, Artificial Intelligence (AI), and social data science to produce representative and trusted estimates from diverse data sources to guide evidence-based policymaking. At the World Bank, the Overseas Development Institute (ODI), and through my PhD studies, I have co-authored national poverty assessments, national development plans, policy briefs, and helped build the policy analysis capacity of over 750 government officials. While these outputs matter a great deal, what defines me is the qualities that make these outputs possible in the first place: 1) I ensure the data we collect truly represents the intended population, and explain clearly how it was collected, so decision-makers can implement the findings. When data is not randomly sampled, I apply statistical adjustments to align it more closely with the target population. 2) I design systems to turn complex statistical reports and techniques into tools sector ministries can actually apply. As a result, over 90% of the 750+ government officials I helped train still use our techniques daily, and many of our publications continue to shape major policy decisions long after I leave the countries. 3) I stay committed to technical innovations. I am dedicating part of my PhD research to developing a machine learning data integration framework that brings together Large Language Models (GenAI), geospatial data, national household surveys, and administrative records to produce small-area welfare estimates that improve how development interventions are targeted. DM for collabs on research and applications within the development policy space.