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 $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
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 $299,998 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will support collaborative research at Carnegie Mellon University to develop new big data algorithms that are robust to adversarial input. The key focus areas include: 1) adversarial robustness in black-box and white-box streaming settings, and 2) adaptive data analysis with bounded space. The research team will also explore emerging attack...
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 $236,099 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 fundamental research on optimization and learning techniques for machine learning models that can handle dynamic and uncertain data distributions, with a focus on two main research thrusts: Developing distributionally robust optimization methods to train learning models that perform well under...
This $100,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to study machine learning (ML)-augmented algorithms that can operate on weak and sparse predictions. The project at the University of California, Merced seeks to understand the trade-offs between prediction quality and performance guarantees when using limited or imprecise data, with the goal of advancing the real-world...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $174,983 to the College of William and Mary to develop new techniques for measuring and controlling memorization in text-attributed graphs. The key research thrusts include: 1) introducing a novel dynamic prompting strategy to more precisely measure memorization rates, 2) proposing a dynamic pruning framework to enable fine-grained control over memorization,...
This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
This $300,000 federal Project Grant award was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of Maryland, College Park. The project aims to develop physics-guided generative artificial intelligence models to better understand and predict complex physical processes like pollution transport, virus spread, and wildfire evolution. By integrating physical equations with generative machine learning...
This $139,660 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 at the College of William and Mary to develop novel online data mining algorithms that can provide transparent and interpretable machine learning models for real-time applications such as crowd movement prediction, disaster monitoring, and pandemic response. Key objectives include: 1) capturing dynamic feature variations to improve model structure, 2) quantifying prediction uncertainty, and 3) indexing and explaining model inference paths. The project aims to bridge the gap between data scientists and domain experts by creating interpretable models that can earn user trust and support critical decision-making. The educational component involves mentoring underrepresented students and developing new coursework in interpretable data mining. The award period is from August 2024 to June 2025.