Dr. Moore’s research focuses on the development, evaluation, and application of artificial intelligence (AI) and machine learning methods for the analysis of high-dimensional biomedical and clinical data. Recent work has focused on automated machine learning (AutoML) that can build analytics pipelines without human intervention. These automated tools serve to democratize ML so that anyone, regardless of training or expertise, can participate in data modeling and predictive analytics. Examples include the Tree-Based Pipeline Optimization Tool (TPOT) and Aliro AI. Current work is focused on integrating AutoML with knowledge graphs using large language models. Application areas include Alzheimer’s disease, cancer, HIV infection, and cardiovascular diseases. 

Dr. Moore has published over 600 per-reviewed papers, reviews, book chapters, and editorials. He is an elected Fellow of the American College of Medical Informatics, American Association for the Advancement of Science, American Statistical Association, International Association for Health Sciences Informatics, and the International Statistics Institute.