This $250,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Program (CFDA 47.049) with a period of performance from September 15, 2025 to August 31, 2028. The grant supports research addressing the question of how the outputs of machine learning or AI algorithms can be used to augment limited datasets to draw meaningful statistical conclusions. The project will investigate this from theoretical, methodological,...
This $100,000 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The purpose of the grant is to study machine learning-augmented algorithms that can operate on weak and sparse predictions, with the goal of enhancing the applicability of these algorithms in real-world scenarios where abundant and accurate training data may be challenging to obtain. The project aims to understand the...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, awarded under CFDA 47.070, provides $350,014 to the University of Illinois to develop responsible language models with rigorous guarantees. The project seeks to enhance the reliability of language models (LMs) through the use of conformal prediction, which provides theoretical guarantees on uncertainty quantification. The research aims to: 1) quantify uncertainty for LMs with theoretical...
This $439,425 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports research to enable the safe deployment of learning-enabled systems that can robustly learn and optimize their behavior based on uncertain human feedback and intent. The key objectives are to: (1) develop methods for providing probabilistic performance guarantees when learning policies from human input,...
This $948,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of new methods for data-efficient decision-focused learning to address uncertainty in various real-world decision-making problems. The research aims to create a general framework for pre-training key components and rapidly fine-tuning them for specific decision-making tasks, such as in public health,...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $450,000 to The Trustees of the University of Pennsylvania from January 2022 through September 2024. The funding supports research to develop new theoretical foundations for uncertainty quantification in non-convex, low-complexity models used in data-driven applications. Specifically, the awardee will conduct research to construct optimal confidence...
This $599,411 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a "Trustworthy Toolbox for Double-Correct Predictive Modeling in Sciences." The project aims to create advanced artificial intelligence (AI) and machine learning (ML) models that can make accurate predictions while also providing transparent, scientifically-grounded rationales for their outputs. This...
This $213,679 federal Project Grant award was issued by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award will fund a research project led by the University of California, Berkeley to develop a compositional framework for reasoning about the probabilistic behaviors of cyber-physical systems (CPS) built with unreliable machine learning components. The framework will leverage stochastic models, quantitative logic-based...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $370,692 to the University of Washington to understand the effects of large language models (LLMs) on the work of online information professionals. The project aims to develop an epistemological framework to characterize the information risks posed by LLM-generated content, as well as proactive and reactive approaches to assessing and detecting...
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...
This $317,381 federal Project Grant was awarded on June 15, 2025 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a five-year research program at Washington State University (WSU) to develop innovative methods for quantifying uncertainty in complex systems using machine learning and conformal prediction. The key goals are to create efficient collaboration between humans and AI systems, enabling informed and confident decision-making in high-stakes applications like medical diagnosis. The research aims to establish calibration techniques, training objectives, and conformal calibration methods tailored to large language models. The developed algorithms will be freely available through open-source software to facilitate widespread adoption. The project also includes educational and outreach initiatives to engage the broader community, including a summer course on uncertainty quantification and efforts to encourage underrepresented minority students in computer science.