Directory

Contributions to Public Health

• AI-guided Sepsis Care in Precision Medicine: We are developing machine-learning and causal inference methods in collaboration with 24 hospitals across the nation to develop ‘precision medicine’ strategies to treat sepsis, a condition that contributes to 1 in 5 deaths globally, with a particular focus on infants and children.
• Computational Modeling to Elucidate RNA-level Regulation in Diseases: We are developing network modeling combined with statistical learning models to elucidate novel roles of complex interactions at the RNA level. This work aims to address the gap in knowledge about post-transcriptional regulation in diverse diseases.
• Infusing Data Analysis Techniques in Biological Study: We develop data-science techniques, AI-driven tools, and statistical inference methods to understand large-scale molecular dynamics in physiological and pathological conditions. This work has broad implications for understanding complex biological processes and diseases
• Exposome Data Analysis using Deep Neural Network Models: We develop advanced computational methods to uncover subtle patterns and relationships within exposome data that may not be apparent through traditional analysis techniques.
• Multi-omics Data Analysis with Biological and Computational Insights: The integration of biological knowledge with advanced computational methods allows for a more comprehensive understanding of complex biological systems, revealing intricate relationships and interactions that may not be apparent through single-omics approaches or purely computational analyses.