Project Grant 2515194
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports research to enhance the adversarial robustness of geospatial-temporal AI models. The primary objectives are to examine vulnerabilities of existing models to adversarial attacks and develop effective solutions to strengthen their resilience. The research team at Michigan State University will work to proactively identify and mitigate...
- 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 $150,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research to enable robust and trustworthy decision-making systems under realistic operational constraints. The project, led by the Regents of the University of Michigan, aims to develop new theories and algorithms for sequential decision-making problems like multi-armed bandits and reinforcement learning. Key research thrusts include: 1)...
- This three-year, $300,000 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), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep learning....
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with the CFDA number 47.070, provides $155,000.00 to the University of Michigan to develop methods for generative artificial intelligence (GenAI) tools to create synthetic but useful data for network and application security tasks. The goal is to enhance the performance of security classifiers, which use machine learning to identify cyberthreats like malware or...
- This federal Project Grant award of $100,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports research on advanced probabilistic models and their application to cutting-edge machine learning techniques. The research aims to bring mathematical rigor and develop new methods related to complex systems in areas such as image processing, reinforcement learning, and generative AI. Key focus areas include: 1) extracting...
- 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 Project Grant award of $268,000.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research to advance the capabilities of large language models (LLMs) through "LLM unlearning" techniques. The research aims to develop methods for the targeted removal of harmful or sensitive content from pretrained LLMs without compromising overall model performance. Key focus areas include optimization algorithms,...
- 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 from the National Science Foundation (NSF) under CFDA 47.070 - Computer and Information Science and Engineering is for $395,927 over the period of Sep 1, 2024 to Aug 31, 2027. The award aims to develop theoretical and algorithmic foundations for building a safe and robust human-AI ecosystem, where machine learning (ML) and artificial intelligence (AI) techniques are used in applications involving humans, such as recommendation systems, lending, and healthcare. The key...
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 reliability of AI in real-world applications like healthcare, scientific research, and security. The research objectives include: 1) developing a theoretical foundation for adversarial training in two-layer neural networks, 2) designing computationally efficient adversarial training methods, and 3) creating robust fine-tuning approaches for pre-trained AI models. This research is expected to yield practical tools applicable across multiple scientific disciplines and contribute to a deeper understanding of statistical learning robustness. The award is effective from August 15, 2025 through July 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $180.0k | 8/5/25 |