Project Grant 2441296

This $129,536 federal Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop advanced algorithms and computational frameworks for large-scale resource allocation problems. The research will tackle three core challenges: effective preference elicitation from users, accounting for preference uncertainty, and enabling efficient computation for scaling to large problem instances.

Key objectives include investigating methods for gathering expressive yet tractable agent preferences, exploring automated prompting techniques to incentivize more complete preference reporting, modeling preference uncertainty using learning-theoretic approaches, and developing simple yet adaptable algorithms that produce balanced, efficient, and robust resource allocations. The project seeks to advance the theoretical foundations and practical applications of balanced and efficient resource allocation, with potential impacts across domains such as workforce scheduling, disaster relief coordination, and student-course matching. No subawards are planned under this award, which has an ultimate completion date of March 31, 2030.