Project Grant 2145542
- This federal Project Grant award of $180,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at Michigan State University to improve the robustness and trustworthiness of artificial intelligence (AI) models. The project aims to establish statistical frameworks for adversarial training in neural networks and develop scalable algorithms that leverage dynamic attack strategies and selective sampling to enhance the...
- The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to New York University (NYU) to investigate the risks of AI-generated code in the software supply chain. The 3-year project, which began on June 1, 2024, aims to: (i) develop techniques to distinguish human-written code from AI-generated code, (ii) measure the prevalence and security implications of AI-generated code in open-source software, and (iii)...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program aims to develop scalable algorithms and methods for improving the explainability and robustness of Artificial Intelligence (AI) models. The $350,000 grant awarded to the University of California, Berkeley will leverage spectral analysis and coding theory techniques to identify key input features and interactions that drive AI model predictions. This...
- This Project Grant award of $148,654 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop statistical tools to improve the reliability of artificial intelligence (AI) used in real-world applications such as automated decision-making, financial forecasting, and neuroscience research. The research will establish mathematically rigorous methods for uncertainty quantification to build trustworthy AI, with applications including enhancing...
- This Project Grant award for $175,000 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The project, titled "Formalizing Human-Interpretable Machine Learning," aims to develop approaches for training artificial intelligence systems, such as self-driving cars, to make their decision-making processes more transparent and interpretable to people. The research will involve pairing...
- This Project Grant award of $347,549 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to advance interactive language systems that can critically reason about and synthesize information from rich knowledge sources. The research aims to develop data-centric and algorithmic approaches to model the complexities of real-world scenarios, where users' questions are ambiguous, answers continuously change based on context, and...
- This Project Grant award for $180,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), aims to advance the mathematical understanding of trustworthy artificial intelligence (AI) algorithms for threat detection. The primary objectives are to investigate few-shot learning techniques, which can build effective models from a very limited number of data samples, and to explore few-shot graph generation methods,...
- This Project Grant award of $150,000 from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program supports research on "Algorithm-Informed Neural Networks (AINNs)". The key objectives are to develop neural network architectures that integrate well-established algorithmic principles to enhance the explainability, reliability, and efficiency of AI systems. By embedding logical steps into AI models, this approach aims...
- 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 $599,999 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a human-centered AI framework for assessing information integrity and providing explanations, with a focus on COVID-19-related content. The project aims to integrate the strengths of expert and non-expert crowdsourced workers with AI models to accurately identify and explain issues with information integrity in...
This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $1,093,364 to develop tools and algorithms to help identify and avoid the use of spurious information in artificial intelligence (AI) models. Specifically, the awardee, New York University, will pursue two technical thrusts. The first focuses on improving the interpretability of AI models to help identify spurious inputs and weight important semantic information. This involves adapting "learning to explain" methods. The second constructs new representation learning algorithms to build models that avoid relying on unstable or spurious relationships in data. The work will study limitations of reweighting estimators and flexible models, as well as assumptions needed to address violations of positivity. Overall, the research aims to advance AI modeling by developing techniques for recognizing and avoiding non-causal correlations that could undermine prediction accuracy.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $546.7k | 6/27/22 |