We are integrating findings from our population studies and laboratory experiments using cutting-edge data science and multi-omics approaches. By combining these data, we are identifying molecular “omics signatures” associated with PFAS exposure. 

Using advanced statistical and machine learning methods, we are improving our understanding of the biological mechanisms underlying PFAS toxicity and improving our ability to predict disease risk. 

Through the integration of epidemiological and laboratory data, this work will deliver a more precise understanding of PFAS toxicity and support more effective prevention, risk assessment, and personalized intervention approaches.