Resource scheduling is a critical component of high-performance computing (HPC) systems. Despite extensive literature on scheduling, new challenges continue to arise due to advancements in hardware, software, and evolving models, metrics, and performance demands. Today’s HPC systems operate on an unprecedented scale, presenting significant challenges for resource management, particularly when facing uncertainty introduced by emerging application characteristics and system-level complexities. Existing schedulers lack robust mechanisms to effectively handle uncertainty, limiting their ability to achieve optimal performance. This project takes on the grand challenge of scheduling HPC resources under uncertainty by introducing an integrated approach that combines algorithm and machine learning (ML). The approach leverages the rigor of algorithmic analysis to provide performance guarantees while utilizing ML’s predictive capabilities to manage uncertainty effectively. The anticipated outcome is a substantial enhancement to current HPC schedulers, enabling more efficient execution of a diverse range of scientific applications, such as neuroscience, medical research, climate modeling, and artificial intelligence. Additionally, the project includes a series of synergistic activities, including outreach programs, curriculum development, and student recruitment, aimed at engaging students from K-12 through graduate levels. These efforts focus particularly on underrepresented and unders