**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** DESPITE THE EXISTENCE OF WELL-ESTABLISHED METHODS (E.G., 24-H DIETARY RECALLS) FOR COLLECTING INDIVIDUAL-LEVEL DIETARY DATA TO IDENTIFY DIETARY PATTERNS (DP), ITS USE IS IMPRACTICAL IN LARGE OBSERVATIONAL STUDIES DUE TO FEASIBILITY ISSUES AND COSTS, TRAINING, AND CODIFYING DATA. NEW TECHNOLOGIES SUCH AS SUPPLEMENTING FOOD RECORD METHODS WITH IMAGES OF FOOD HAS EMERGED, BUT MORE RESEARCH IS STILL NEEDED IN DIETARY ASSESSMENT TO APPROPRIATELY MODEL THE COMPLEXITY OF DIET TO UNDERSTAND DIETARY PATTERNS. IN THIS POSTDOCTORAL FELLOWSHIP APPLICATION, I WILL INVESTIGATE HOW MACHINE LEARNING (ML) TECHNIQUES CAN IMPROVE OBJECTIVITY FROM CURRENT METHODS OF DIETARY ASSESSMENT. I WILL BE EXAMINING USING PHOTOS AND BIOMARKERS AS OBJECTIVE MARKERS OF DIETARY PATTERNS. THE SPECIFIC AIMS ARE, (1) DEVELOP ML TECHNIQUES TO IDENTIFY DIETARY PATTERNS FROM PHOTOS OF FOODS FOR THE ADOPTION OF PHOTO-BASED DIETARY ASSESSMENT IN NUTRITION RESEARCH, (2) COMPARE IDENTIFICATION OF BIOMARKERS ASSOCIATED WITH VARIOUS DP VIA CONVENTIONAL STATISTICAL MODELS TO MACHINE LEARNING APPROACHES. THIS PROJECT WILL DEVELOP THE ACADEMIC AND PROFESSIONAL SKILLS OF THE PROJECT DIRECTOR WITH THE DIRECTION OF THE PRIMARY MENTOR, DR. DAVID CRANDALL, AND COLLABORATIVE MENTORS, DR. DANIELLE LEMAY AND DR. LAUREN O'CONNOR. THIS PROJECT FALLS UNDER 'FOOD SAFETY, NUTRITION, AND HEALTH' OF AFRI FARM BILL PRIORITY AREA AND WILL ADDRESS THE OBJECTIVES UNDER THE PROGRAM AREA, DIET, NUTRITION, AND THE PREVENTION OF CHRONIC DISEASES. I WILL BE DEVELOPING, IMPLEMENTING, AND EVALUATING INNOVATIVE RESEARCH, THROUGH INTEGRATING ML INTO DIETARY ASSESSMENT TO ASSESS DIETARY PATTERNS, WHICH COULD THEN GUIDE POLICIES THAT PREVENT AND CONTROL DIET-RELATED CHRONIC DISEASES.