Machine learning pointed at biology and public health, mostly with the Nolan Lab and the Valafar Lab at the University of South Carolina.
The fun part: heatmaps of where the model is looking. Red blob = parasite.
Medical imaging · Deep learning Paper in review
A deep learning model that flags Trypanosoma cruzi, the parasite behind Chagas disease, in smartphone photos of blood smears: a step toward low-cost screening where specialized lab equipment is out of reach.
With Nolan Lab & Valafar Lab
Epidemiology · Remote sensing Published 2026
Combining remote sensing and machine learning to show that human dengue outbreaks in Mexico track geographic expansion, not viral intensification.
With Nolan Lab & Valafar Lab
Neuroimaging
Used deep learning to explore how gray and white matter relate to cognitive health.
Forecasting
Part of the CDC's 2022 forecasting challenge: a Bayesian model predicting neuroinvasive West Nile virus cases in every US county.
PLoS ONE, 2023 ↗Challenge evaluation, Parasites & Vectors 2025 ↗
Genomics
With UofSC's School of Public Health: next-generation sequencing and comparative genomics to find diagnostic targets for spotted fever group rickettsioses, plus tick surveillance in El Salvador and South Carolina.
Structural biology
Extending the predictive power of AlphaFold2 with new techniques and datasets, and making the Protein Data Bank searchable by geometry in real time.
Virology
Exploring the spike–ACE2 interaction across SARS-CoV-2 variants, and the non-structural protein Nsp1.