THE APPLICATION OF DATA SCIENCE HAS GAINED PROMINENCE IN SOME DISCIPLINES BUT FOOD RESEARCH AND DEVELOPMENT. CURRENT ADVANCES AND TRENDS POINT TO NOVEL DATA TOOLS, ANALYTICAL APPROACHES, AND TECHNOLOGIES IN ADDITION TO TRADITIONAL METHODS TO HANDLE COMPLEX SCENARIOS. IN FOOD SCIENCE RESEARCH, THESE SCENARIOS ARE AMPLIFIED BY THE VAST ASSORTMENT OF FOOD CHOICES CONSUMED IN VARYING QUANTITIES, COMBINATIONS, BY CONSUMERS IN DIFFERENT REGIONS, AND STAGES IN LIFE. IN ADDITION TO THIS WIDE VARIABILITY, CURRENT COMMONLY USED METHODS TO NAVIGATE AND INTERPRET THIS DATA REMAIN MOSTLY CUMBERSOME AND TIME-CONSUMING. THE PROPOSED INTERDISCIPLINARY PROJECT SEEKS TO FORMULATE AND USE INTEGRATED MACHINE LEARNING (ML) APPROACHES, AND QUANTITATIVE ANALYSIS CONCEPTS IN FOOD RESEARCH. THIS PROJECT LEVERAGES DOMAIN-SPECIFIC KNOWLEDGE AND DATA-DRIVEN APPROACHES TO AID FOOD PRODUCT AND PROCESS DESIGN, CONSUMER HEALTH, AND SAFETY TO BUILD AND MAINTAIN OPTIMIZED MODELS TO ACCELERATE DISCOVERY AND INNOVATION.THIS PROJECT USES INNOVAIVE DATA SCIENCE APPROCHES IN FOOD SCIENCE RESEARCH, EDUCATION AND TRAINING AND EQUIP BOTHSTUDENTS AND FACULTY WITH COMPUTATIONAL THINKING SKILLS AS WELL AS SKILLS TO MAKE INFERENCES FROM DATA COLLECTED. THIS PROJECT PURSUES THE FOLLOWING OBJECTIVES; (1) DEVELOP AN APPROACH FOR WORKING ON STREAMING DATA IN FOOD RESEARCH AND USE SELECTED ML ALGORITHMS, MAINLY CLUSTERING AND PREDICTIVE ANALYTICS TO EXPLORE SPECIFIED SCENARIOS; (2) IDENTIFY APPROPRIATE SUPERVISED LEARNING TOOLS; (3) DEVELOP METHODS OF IDENTIFYING DISTRIBUTIONAL CHARACTERISTICS OF VARIOUS VARIABLES AND; (4) TRAIN STUDENTS AND FOSTER FACULTY PROFESSIONAL DEVELOPMENT WITH COMPETENCIES IN DATA SCIENCE. WE PRESENT AS EXAMPLES APPLICATION OF DATA-DRIVEN APPROACHES TO FOUR FOOD CHEMISTRY RESEARCH CASE STUDIES TO SPARK NEW DIRECTIONS. THE FOOD INDUSTRY, GOVERNMENT, AND OTHER STAKEHOLDERS STAND TO BENEFIT FROM WORKING CROSS-FUNCTIONALLY TO UNLOCK OPPORTUNITIES THAT SUPPORT DATA-DRIVEN AND SCIENTIFIC DECISION MAKING IN FOOD RESEARCH AND DEVELOPMENT AND POLICY.