Using network science to study microbial communities, ecological interactions and environmental change.

Studying how biological and symptom networks can improve our understanding of mental health and cognition.

Investigating how genetics, nutrition and social context interact to shape health across populations.

Methods for turning transcriptomic and multi-omic data into reproducible, interpretable biological networks.

Combining network medicine, machine learning and experimental evidence to identify therapeutic opportunities.

Developing interpretable and generalizable AI approaches for molecular, clinical and population health data.

Connecting molecular interactions, disease mechanisms and clinical phenotypes to improve biomedical prediction.