Research

My current research sits at the intersection of multi-omics—focusing on exposomics, metabolomics, and proteomics—and data science, with the goal of uncovering complex biological mechanisms. I utilize statistical methods and machine learning approaches to study the effects of environmental exposures on human health, particularly in the context of biomarker discovery.

One of my recent studies examines the relationship between per- and polyfluoroalkyl substances (PFAS) exposure and SARS-CoV-2 IgG antibody levels in pregnant individuals. Using untargeted metabolomic profiling and high-dimensional mediation analysis, we identified key metabolites mediating this association, offering insights into the biological pathways involved. By developing computational frameworks to analyze high-dimensional biological data and integrating multi-omics datasets, this type of research could advances our understanding of how environmental contaminants can impact human health.

I believe that leveraging data-driven insights is key to improving public health outcomes and driving advancements in personalized medicine.