Project Grant 2515333
- This federal Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program aims to develop tools and methods to improve decision-making and enhance interpretability in reinforcement learning (RL) algorithms operating in complex, data-limited environments. The $154,999 award supports research to address challenges in ensuring RL systems are statistically robust, interpretable, and socially responsible for real-world...
- 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...
- This Project Grant award, titled "CAREER: FOUNDATIONS OF TRUSTWORTHY SEQUENTIAL DECISION-MAKING: PRIVACY, ROBUSTNESS, AND FAIRNESS," is funded by the National Science Foundation (NSF) Engineering program (CFDA 47.041). The $500,000 award, effective from October 1, 2025 to September 30, 2030, aims to advance trustworthy artificial intelligence and machine learning (AI/ML), particularly reinforcement learning (RL), in applications such as healthcare, education, and commerce. The...
- 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...
- The National Science Foundation (NSF) awarded a $375,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the Regents of the University of Michigan, Office of Research and Sponsored Projects, doing business as the University of Michigan. The grant, awarded on October 1, 2023, aims to develop foundational technologies for safe Reinforcement Learning (RL)-enabled systems, integrating research and education. The project focuses on three key thrusts: (1)...
- 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...
- This Project Grant award of $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research on developing machine learning models for structural decision-making. The goal is to create models that can capture an agent's preferences and understanding of environmental dynamics, enabling better prediction and adaptation of decision-making in complex real-world scenarios. The research will explore methods for...
- 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 $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to the Regents of the University of Michigan for the project "WASSERSTEIN PDES AND APPLICATIONS TO OPTIMIZATION AND LEARNING". The project aims to advance artificial intelligence and optimization through the study of partial differential equations on Wasserstein space, addressing fundamental challenges in machine learning that support...
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) trust-aware procedures that account for human behavioral factors, 2) robust transfer learning methods to address distribution shifts, and 3) model-agnostic algorithms that adapt to unknown environmental structures. The project will also provide training opportunities for students in STEM fields and engage the general public on advances in data science and AI. Work will be conducted at the University of Michigan in Ann Arbor from August 2025 through July 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/14/25 |