Project Grant 2416897

Award Date 9/1/24
Completion Date 8/31/27
Dollars Obligated $800K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Urbana, IL 61801, USA
Similar Awards
This $350,014 Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, seeks to develop responsible language models (LMs) with rigorous guarantees. The award aims to explore the use of conformal prediction to quantify uncertainty in LMs, merge conformal prediction's robust uncertainty estimation with LMs' self-correction capabilities, and introduce uncertainty-based reliability measures and error...
The National Science Foundation (NSF) awarded a $260,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Illinois for the project "COLLABORATIVE RESEARCH: SLES: VERIFYING AND ENFORCING SAFETY CONSTRAINTS IN AI-BASED SEQUENTIAL GENERATION". This 3-year project aims to develop formal verification methods and constrained generation techniques to ensure the safety and reliability of AI models used for sequential data processing...
This $800,000 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to establish a computational foundation for safe Graph Neural Networks (GNNs). The 3-year project investigates the end-to-end safety of GNNs, which are a family of deep learning models for interrelated, graph-structured data. The research aims to develop new theories, algorithms, and evaluation methods to enable safer...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $370,692 to the University of Washington to study the effects of large language models (LLMs) on online information work. The key goals are to model the risks that LLMs pose to effective online discourse and develop tools to help information professionals assess LLM-generated content. Through interviews, co-design activities, and surveys...
The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of South Carolina. The grant, with a period of performance from October 1, 2024 to September 30, 2027, focuses on enhancing security and mitigating harm in AI-generated vision language models. Key technical objectives include: 1) Developing a prompting framework for detecting harmful content provenance in AI-generated vision...
The National Science Foundation (NSF) awarded a $793,065 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Wisconsin System for the project "SLES: Foundations of Safety-Aware Learning in the Wild." The project aims to develop novel machine learning algorithms and theoretical guarantees that can reliably detect and handle out-of-distribution data encountered by AI models deployed in dynamic, unpredictable environments. This...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $348,227 to the University of Delaware to conduct research focused on improving the scalability and effectiveness of large language models (LLMs) for healthcare applications. The project seeks to develop evaluation frameworks and mitigation techniques to address challenges like factual and faithfulness hallucinations in LLM outputs. Additionally, the award...
The University of Illinois was awarded a $500,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems to support research activities related to reinforcement learning in non-stationary environments. Specifically, the grant will fund the development of techniques for safe reinforcement learning with fast adaptation and disturbance prediction capabilities. The work advances the National Science Foundation's Computer and Information Science and...
This $400,000 Project Grant was awarded on July 1, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to the Regents of the University of Michigan to conduct collaborative research on the theoretical foundations of compositional learning in large language models based on transformer architectures. The research aims to investigate three key areas: model expressivity, statistical learning theory, and optimization, with...
The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...

The National Science Foundation (NSF) awarded a $800,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Illinois Urbana-Champaign. The 3-year grant, effective September 1, 2024, focuses on enhancing the safety of large language models (LLMs) used in high-stakes applications. The project aims to develop quantifiable safety measures and algorithms to detect and mitigate unsafe behaviors in LLMs, such as providing false or inconsistent information. Key technical thrusts include robust-confidence safety, self-consistency safety, and alignment safety. The project will test these methods using the open-source LLMFlow framework to enable practical applications and community access. In addition, the grant will support the development of a graduate-level course on trustworthy AI to promote diversity in this field at the University of Illinois. No sub-awards are planned for this award.

Generated 6/17/25, 3:28 AM