I develop causal inference and AI methods for clinical and surgical applications.
Causal Inference under Interference with Learned Exposure Mappings
When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation
Biomedical research increasingly brings together data from many sources, including clinical, laboratory, behavioral, socioeconomic, and genomic data.
I develop representation learning methods for integrating these data while capturing latent structure that can be useful for biomedical research. I am particularly interested in using learned representations to study complex relationships and potential mechanisms in high-dimensional health data.
Earlier in my research, I used machine learning and statistical methods to study environmental exposures, population health, and social vulnerability.
This work combined large-scale data on air pollution, weather, traffic, climate, health, and sociodemographic factors.
