Project Grant 2543553
- Federal Project Grant Award Summary Cornell University received a $451,594 CAREER grant from the National Science Foundation's Division of Computing and Communication Foundations (CFDA 47.070: Computer and Information Science and Engineering program) awarded on April 15, 2026, with completion targeted for March 31, 2031. The project develops novel algorithms leveraging the Gittins Index—a classical theoretical tool for optimal prioritization—to enable efficient artificial intelligence (AI) model...
- Federal Project Grant Award Summary Cornell University's Office of Sponsored Programs received a $900,000 Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective September 1, 2025, through August 31, 2028. This research initiative investigates how generative artificial intelligence (AI) tools affect team dynamics and collaboration...
- Federal Grant Award Summary Cornell University's NSF Engineering program project grant (CFDA 47.041), titled "CAREER: Robust Learning via Optimal Transport," received $670,000 in funding with a performance period from April 1, 2026, through March 31, 2031. The project will deliver advanced mathematical frameworks and computational tools designed to enhance the robustness and reliability of artificial intelligence (AI) systems in unpredictable, real-world conditions. Key deliverables...
- Federal Grant Award Summary Cornell University received a $399,998 Project Grant from the National Science Foundation (NSF) Division of Behavioral and Cognitive Sciences under the Social, Behavioral, and Economic Sciences program (CFDA 47.075), effective September 1, 2025 through August 31, 2028. This collaborative research initiative develops and delivers new online infrastructure tools integrated into existing behavioral science platforms (Dallinger and PsyNet) that enable large-scale...
- Federal Project Grant Award Summary Cornell University received a $300,000 Project Grant from the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective July 1, 2025 through June 30, 2028. This collaborative research initiative focuses on developing evaluation concepts and automated assessment technologies for Large Language Models (LLMs). The project will identify a...
- Federal Grant Award Summary Cornell University received a $333,333 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems (CISE Program, CFDA 47.070) awarded October 1, 2025, with completion targeted for September 30, 2028. This collaborative research initiative develops a novel neurosymbolic programming framework, designated Foundation Model Programming, designed to generate symbolically interpretable scientific hypotheses from high-dimensional...
- Federal Grant Award Summary Cornell University received a National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program grant (CFDA 47.070) awarded on August 15, 2025, for the CAREER project titled "Watch, Interact, Collaborate: Online Learning Paradigms for Robot Apprentices." The project, scheduled for completion by July 31, 2030, will develop and deliver a comprehensive framework enabling robots to learn and adapt tasks in real-time through...
- Federal Grant Award Summary Cornell University received a $520,000 Project Grant from the National Science Foundation (NSF) Division of Computer and Network Systems under the Computer and Information Science and Engineering program (CFDA 47.070), effective January 1, 2026 through December 31, 2028. This award supports research investigating security and privacy vulnerabilities in embeddings—the mathematical vector representations fundamental to artificial intelligence (AI) systems including...
- Federal Grant Award Summary Cornell University received a $222,569 Project Grant from the National Science Foundation's (NSF) Division of Computing and Communication Foundations under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective October 1, 2025, through May 31, 2029. The grant supports a CAREER award focused on developing machine perception systems capable of forming cross-modal associations between vision, touch, and sound to learn material...
- Federal Grant Award Summary New York University received a $347,549 CAREER award from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CFDA 47.070) program, effective September 1, 2025, through August 31, 2030. This project grant supports the development of interactive language systems that critically reason about textual sources to provide high-quality, current information to users. The research...
Cornell University received a $367,129 CAREER (Careers in Research and Education) award from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective July 1, 2026 through June 30, 2031. The award supports research addressing the challenges of aligning artificial intelligence (AI) systems with heterogeneous user preferences. The project will deliver two primary software tools: a website comparing AI models to assist users in selecting appropriate AI tools for their specific needs, and an AI-enhanced tool providing real-time information on group opinions in participatory processes. These tools will translate research findings into practical applications for broader public benefit. The project also includes significant educational and research components. The awardee will develop new courses and educational materials training students in the mathematical foundations of preference-aware AI, integrating concepts from computational social choice theory with generative AI methodologies. The research effort will analyze AI alignment methods through distortion metrics to quantify shortcomings in dominant alignment approaches such as reinforcement learning from human feedback, while designing alternative algorithms that are provably robust to disagreeing user preferences and capable of achieving sensible compromises between conflicting interests.Federal Project Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $367.1k | 4/20/26 |