Groups

Bio-Data Science

We advance bio-data science and AI for human and environmental health by integrating machine learning, knowledge-driven data integration, graph-based AI, and grounded large language models. Our work enables transparent, explainable, and reproducible computational research to support scientific discovery and informed decision-making.

Computational Systems Biology

We harness the power of multi-omics data integration to unravel chemical-based molecular perturbations. By pioneering cutting-edge methods for pathway enrichment and chemical grouping, we push the boundaries of omics data application in regulatory risk assessment and decision-making, while adhering to the FAIR principles.

Toxicokinetic Modeling

With an emphasis on mechanistic understanding, we develop toxicokinetic models elucidating chemical behavior in aquatic and terrestrial organisms. We also aim to minimize reliance on animal testing by developing predictive tools and in vitro - in vivo extrapolation techniques.