AI for Health and Biological Systems

This area applies systems theory, computational modeling, and AI to understand biological processes and improve healthcare and public-health decisions. Research includes the modeling, identification, analysis, and control of biological networks across molecular, cellular, physiological, and population scales. Computational and systems-biology methods are combined with machine learning to extract knowledge from genomic, imaging, clinical, wearable-sensor, and other multimodal data. Applications include personalized diagnosis and treatment, medical devices, therapeutic control, disease-progression modeling, clinical decision support, healthcare operations, epidemiology, and public-health intervention design. Because decisions in these settings directly affect people, the area emphasizes uncertainty quantification, causal validity, interpretability, privacy, fairness, safety, and effective collaboration with clinicians, biologists, and public-health experts.