Highlights from over a decade of research in spatial statistics, global health, climate science, and beyond.
Produced the first high-resolution (5×5km) maps of child stunting, wasting, and underweight prevalence across 51 African countries, drawing on survey data from over one million children collected between 2000 and 2015. The maps revealed stark subnational inequalities hidden by national-level averages, providing actionable intelligence for targeting interventions.
Cited by Kofi Annan in a Nature World View letter. Published alongside a companion paper mapping educational attainment across Africa.
Extended the geospatial mapping framework to all low- and middle-income countries worldwide, producing comprehensive estimates of child growth failure that informed the Sustainable Development Goals agenda. This expansion required developing scalable spatial modeling techniques capable of handling vastly heterogeneous data sources across diverse geographic contexts.
Mapped annual geospatial estimates of anemia prevalence in women aged 15–49 across 82 low- and middle-income countries between 2000 and 2018. The analysis identified regions with the greatest burden and most rapid changes, providing evidence to guide targeted public health interventions and resource allocation.
Authored a comprehensive statistical review comparing Template Model Builder (TMB) and R-INLA for fitting complex spatial and spatio-temporal models. Includes a large-scale simulation study evaluating both approaches for continuous spatial models via the SPDE approximation, providing practical guidance for applied spatial statisticians choosing between these tools.
Developing a joint spatial modeling framework for simultaneously estimating cancer incidence and mortality rates, with application to breast cancer across European countries. A collaboration with researchers at the International Agency for Research on Cancer (IARC), leveraging shared spatial structure between incidence and mortality to improve estimation.
Developing novel spatial statistical models for spatial transcriptomics datasets, extending traditional geostatistical methods to the rapidly growing field of spatially-resolved gene expression data. This work bridges spatial statistics and computational biology, applying Gaussian process methods to understand the spatial organization of gene expression within tissues.
Invited presentation at Yale University, Computational Biology Group (2024).
Peer-reviewed journal publications. View full CV for the complete list.
* denotes co-lead authors