Leveraging Optimization and AI to Improve Disease Analysis

Dr. Shamir's research focuses on applying computational biology and machine learning to biomedical challenges, from optimizing Parkinson’s disease diagnostics to improving genetic risk prediction across diverse populations.

The talk will highlight efforts to improve the assessment of Parkinson’s disease by refining existing diagnostic scales, as well as the development of PRANA, a deep-learning framework designed to enhance polygenic risk prediction across diverse populations. Together, these projects demonstrate how data-driven approaches can advance disease monitoring and personalized medicine.