Artificial Intelligence and Precision Nutrition Training Program

Project Summary Precision nutrition approaches aim to move away from a one-size fits all approach to identify individual-level dietary and nutritional intake for optimal health by accounting for individual variability in genes, phenotype, environment, and lifestyle for each person (NIH) – i.e., tailoring nutrition interventions and/or recommendations

to individuals by accounting for the complex nutritional ecology reflecting the interaction of a complex system. The components of precision nutrition include assessments of a number of biological, clinical, social, and environmental parameters including the multi-omics, genomics, proteomics, and metabolomics, as well as

account for sustainability. Approaches relying on Artificial Intelligence (AI) and machine learning (ML) are increasingly being used across the research realm including in nutrition to analyze and interpret such complex data. The 2020-30 Strategic Plan for NIH Nutrition Research, developed by the NIH Nutrition Research Task

Force, identifies a key need to produce a nutrition workforce that is trained to solve the most pressing problems in nutrition and health, by applying novel methods and working in multidisciplinary teams. We propose to leverage our existing strengths and initiatives at Cornell to implement a novel program to train the next

generation of scientists in the domains of AI and precision nutrition to address the future needs for the nutrition workforce. The training program, with positions for 4 predoctoral and 1 postdoctoral trainees per year, is built on the outstanding doctoral programs in the multiple departments at Cornell University participating in this

application, which emphasize multidisciplinary and integrative scholarship across the biological, physical, behavioral, data, and social sciences. The 23 trainers represent the broad range of disciplines necessary to achieve the goals of the training program and include renowned scientists with expertise spanning from

nutrition, medicine, health, computing and information sciences, bioinformatics, population genetics, and computational biology. The trainers have active research programs and excellent training records. The proposed training program includes a core curriculum tailored for students starting in nutrition and minoring in

computer science and for those starting in computer sciences and minoring in nutrition. We have also included plans for a new AI and Precision Nutrition course with hands on analyses that is already being put in place and will be ready for roll out in the next academic year. The infrastructure to support the proposed training program

is outstanding, with added strengths from faculty members across the campus. Further, the new AI innovation hub at the Cornell Center for Precision Nutrition and Health will provide a unique opportunity for diverse trainees to come together and prepare the next generation of the nutrition scientific workforce.