**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** FOODBORNE ILLNESS IMPACTS NEARLY 50 MILLION PEOPLE IN THE UNITED STATES PER YEAR, CAUSING SEVERE ECONOMIC DAMAGES AND HEALTHCARE BURDENS. THERE IS AN URGENT NEED FOR MORE EFFECTIVE STRATEGIES TO MANAGE PATHOGEN TRANSMISSION ACROSS FOOD AND AGRICULTURAL SYSTEMS. THIS PROPOSAL AIMS TO DEFINETHE SPATIAL AND TEMPORAL DISTRIBUTIONS OF EXPANSIVE GENOMIC DIVERSITY OF FOODBORNE PATHOGENS BY INTEGRATING OPEN-SOURCE MOLECULAR DATA WITH ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) TECHNOLOGIES, IN ORDER TO INFORM PRACTICAL APPLICATIONS THAT WILL PROMOTE FOOD SAFETY. FOCUSING ON A VARIETY OF PATHOGENS THAT PRESENT UNIQUE RISKS (I.E.,CAMPYLOBACTER JEJUNI,CRONOBACTER SAKAZAKII, ANDLISTERIA MONOCYTOGENES), WE WILL BUILD AND VALIDATE A COMPUTATIONAL WORKFLOW TO UNDERSTAND HOW SELECTIVE PRESSURES ACROSS FOOD CHAINS DRIVE BACTERIAL ENVIRONMENTALTROPISMS (E.G., GENES UNDERLYING PERSISTENCE, ANTIMICROBIAL RESISTANCE, OR VIRULENCE LINKED TO SPECIFICSOURCE TYPES). LEVERAGING PUBLICLY AVAILABLE GENOMIC AND PHENOTYPIC DATA WILL ENABLE HIGH-THROUGHPUT IDENTIFICATION OF NEW MOLECULAR MARKERS ASSOCIATED WITH FOODBORNE PATHOGEN TRANSMISSION AND EVOLUTION. THESE DATA WILL FURTHER SERVE AS KEY REFERENCE MATERIAL TO IMPROVE STRATEGIES FOR PATHOGEN MOLECULAR SURVEILLANCE WITH METAGENOMICS. OVERALL, THE ANALYTICAL FRAMEWORK THAT WEDEVELOP AND PROMOTE FOR USE BY THE FOOD SAFETY RESEARCH COMMUNITY WILL HAVE APPLICATIONS THAT EXTEND TO BROADER FOODBORNE PATHOGEN GROUPS AND EMERGING FOOD SAFETY CONCERNS.