This project develops powerful new tools for understanding today’s most complex data, leveraging cutting-edge artificial intelligence (AI) techniques to help data analysts across diverse fields make informed, automated decisions. Modern science increasingly depends on massive and intricate datasets. Yet many of these datasets are messy, heterogeneous, and too large or irregular for traditional methods to manage effectively. This research introduces novel approaches to analyze such data -- methods that are fast, flexible, explainable, and AI-powered. These tools help scientists and decision makers identify patterns, quantify uncertainty, and make better data-driven decisions. In parallel, the project advances public education in data science and mathematics by creating learning opportunities for high school and college students and by bringing cutting-edge ideas into classrooms and community events. In this way, the project invests in the next generation of talent and underscores the role of AI-enhanced statistical reasoning in solving urgent challenges across science, health, and industry. Technically, the project develops a unified framework for graph-based statistical and machine learning inference in two fundamental classes of complex data: manifold data, which exhibit hidden geometric structures, and mixture data, which arise from overlapping subpopulations. Three core research aims guide the effort. The first develops methods to estimate causal effects in structured d