Project Grant 2552063
- This $150,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of foundational principles, algorithms, and tools for causal decision-making systems. Researchers at Columbia University will enrich traditional artificial intelligence formalism with causal modeling to enable more efficient, robust, and explainable decision-making by autonomous systems. Key deliverables include integrating...
- The National Science Foundation Division of Computing and Communication Foundations awarded Case Western Reserve University $420,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop methods for understanding and leveraging causal knowledge within foundation models. The project addresses the gap between pattern recognition in large AI systems and true causal reasoning, which is critical for reliable deployment in healthcare,...
- The National Science Foundation Division of Computing and Communication Foundations awarded New York University $500,000 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretically sound and scalable deep reinforcement learning methods for imperfect-information games—settings where decision makers must act without full knowledge of others' information states. The project delivers three primary components: sound policy-gradient...
- The National Science Foundation Division of Computing and Communication Foundations awarded Brown University $442,685 on September 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for research on inferring AI specifications from imperfect human input. The project develops computational foundations for designing AI systems that can interpret and reason about human intent when instructions are unclear, rigid, vague, or incorrect. The research integrates...
- The National Science Foundation awarded $739,500 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to The Trustees of Columbia University in the City of New York for the project "III: TOWARDS CAUSAL FAIR DECISION-MAKING" from May 15, 2021 through April 30, 2024. The funding will support investigator-initiated research and education in computing, communications, and information science and engineering to advance the development and use of...
- The National Science Foundation Division of Computing and Communication Foundations awarded The Johns Hopkins University $206,849 under the Computer and Information Science and Engineering program (CFDA 47.070) on October 1, 2025, for research on causal reasoning using information-theoretic methods. The project runs through December 31, 2027, with place of performance in Baltimore, Maryland. The research develops algorithms that identify information-theoretically simple explanations of causal...
- The National Science Foundation Division of Computing and Communication Foundations awarded the University of Southern California $871,225 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretical foundations for multi-agent learning systems in artificial intelligence. The project advances the theory of online optimization and learning within multi-agent systems through three research thrusts. The first thrust characterizes optimal...
- The University of Chicago received a $545,359 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective August 1, 2025, through July 31, 2028. This research initiative addresses critical limitations in generative artificial intelligence (AI) systems by developing causal concept models that enable robust causal reasoning and concept discovery. The...
- The National Science Foundation Division of Computing and Communication Foundations awarded Massachusetts Institute of Technology $500,000 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for collaborative research on reinforcement learning methods for imperfect-information games. The project develops theoretically sound, scalable policy-gradient algorithms and decision-time planning methods that enable deep reinforcement learning to operate...
- The National Science Foundation Division of Computing and Communication Foundations awarded Duke University $420,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretical and algorithmic frameworks for off-dynamics reinforcement learning—methods that enable intelligent systems to learn in simulated or indirect environments and transfer that knowledge reliably to real-world deployment scenarios with different transition...
The National Science Foundation Division of Computing and Communication Foundations awarded Syracuse University $371,641 on July 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop approximate causal reinforcement learning methods for artificial intelligence decision-making systems. The project addresses two fundamental gaps that limit AI reliability in real-world deployment. First, historical data used to train decision-making systems are shaped by hidden confounding factors never recorded during data collection. Second, the conditions under which training data are gathered rarely match deployment environments, causing systems to perform poorly once deployed or fail to transfer when circumstances change. The research develops a framework bridging formal causal inference theory with practical reinforcement learning by modeling environments as structural causal models while working with only simplified representations available to practitioners. The methods will derive reliable performance bounds for candidate policies from confounded historical data and use these bounds to guide safe policy improvement, aiming to make AI decision-making more efficient, robust, generalizable, and interpretable. Work is performed at Syracuse University in Syracuse, New York. The award runs through June 30, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $371.6k | 7/20/26 |