PROJECT SUMMARY The answers to many fundamental questions in medicine and biology currently lie buried inside data collections that are too large and heterogeneous to be analyzed and visualized by traditional approaches. As institutions such as the Mount Sinai Health System continue to make sizable investments in early detection of diseases, expansion of more effective population health approaches, and drug/treatment protocol development for personalized medicine, leveraging a wide range of data from imaging to pathology, from genomic data to electronic health records, there is enormous pressure for rapid results. However, the capabilities of AI tools are often overstated by vendors, and the deployment of these tools without sufficient training and understanding of their validity and limitations can result in wasted resources and harmful patient outcomes. Here we propose a supplement for T32 GM 062754, “Integrated Training in Pharmacological Sciences,” which funds predoctoral trainees who focus on pharmacology while pursuing their PhDs in Biomedical Sciences at Mount Sinai. With funds from this supplement, Dr. Hayit Greenspan, recently recruited to Mount Sinai from Tel Aviv at the Professor level, will develop a course dedicated to data science for AI/ML in biomedicine. Support is requested for a course module on competencies needed to make data FAIR and AI/ML-ready, and in the skills required to collaborate effectively with researchers in information sciences and AI/ML. The proposed new course synergizes extremely well with the existing efforts of the training program, which has emphasized quantitative competencies for more than a decade. Although we are a program in pharmacological sciences rather than in computational biology per se, all trainees are required to learn fundamental concepts in programming, mathematical modeling, and systems pharmacology during the first year core curriculum. The didactic focus, however, has been on mechanism-based mathematical models, with only more limited offerings in AI and ML thus far. The new course will fill a substantial need in the current curriculum by educating our predoctoral students on contemporary issues on data storage and availability that prevent AI/ML from reaching its full potential in biomedicine. By teaching our students these issues and emphasizing the principles that make data FAIR-compliant, we will facilitate future collaborations with data scientists and AI experts by allowing groups with different perspectives to understand the relevant issues and speak the same language.