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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program grant award, CFDA 47.070, provides $236,099 to the University of Wisconsin-Madison to conduct research on optimization and learning algorithms that can handle dynamic and uncertain data distributions. The project aims to develop robust computational techniques for supervised learning tasks, such as regression and classification, that can perform well even when the training and test data...
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,...
The National Science Foundation (NSF) is providing a $206,382 Project Grant under its Computer and Information Science and Engineering (CISE) program to the Illinois Institute of Technology (IIT) Sponsored Research and Programs Division. The objective of this 5-year award is to design a trustworthy, flexible, and generalizable machine learning framework that can provide robustness against common privacy and security attacks. The project will develop novel information-theoretic representation...
This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
The National Science Foundation (NSF) awarded a $299,993 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the University of Chicago. The grant supports a collaborative research project on the "Foundations of Few-Round Active Learning" in supervised machine learning. The key objectives are to advance active learning algorithms and improve understanding of their capabilities in scenarios with limited interaction rounds. The research aims...
This $582,000 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at Iowa State University of Science and Technology to develop reliable and efficient distributed machine learning model training techniques. The goal is to create redundancy and coding-theoretic methods that can tolerate worker node failures and slowdowns, while also minimizing communication overhead. This research aims...
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...
This National Science Foundation (NSF) Division of Information and Intelligent Systems Project Grant, CFDA 47.070 Computer and Information Science and Engineering, provides $599,594 in funding to Trustees of Indiana University to develop new machine learning and planning algorithms. The project will focus on the interaction between approximations in the inference process and the implications for planning quality. The algorithmic solutions will be evaluated across problems in AI planning, optimal...
The National Science Foundation awarded a $675,271 Project Grant to the Georgia Tech Research Corporation from October 1, 2021 to September 30, 2024 under the Computer and Information Science and Engineering program (CFDA 47.070). The grant supports collaborative research on principled uncertainty quantification in deep learning models for time series analysis. The Computer and Information Science and Engineering program aims to advance computing and informatics research. This award will further...