Project Grant 2511684
- This $220,003 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research on versatile and scalable sampling algorithms for high-dimensional probability distributions. The project aims to develop innovative sampling methods and analytical tools that can enable better modeling, simulation, and inference for complex systems with uncertainties. The research will explore strategies to improve the scalability and...
- 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...
- The National Science Foundation (NSF) awarded Carnegie Mellon University a $567,000 Project Grant under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The grant, which runs from September 1, 2025 to August 31, 2028, supports the "AIMING: A NEURO-SYMBOLIC APPROACH TO MECHANIZED MATHEMATICAL REASONING" project. This project aims to develop novel AI techniques that combine machine learning and symbolic AI methods to advance mathematical reasoning and the...
- This $100,000 Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research to elucidate the fundamental mechanisms underlying flow-based generative AI models, such as diffusion models, and extend their capabilities to handle complex data types. The research aims to: (1) understand why trained flow models often generalize better than theoretically expected, using tools from geometry, ODE, manifold learning, and deep learning theory; and (2)...
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at Carnegie Mellon University to advance the theoretical and applied frontiers at the intersection of probability, geometry, and combinatorics. The project focuses on three main research directions: 1) enabling efficient statistical inference for geometric probability distributions, 2) analyzing minimum lengths of combinatorial structures in...
- This $200,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will enable Carnegie Mellon University (CMU) to develop advanced spatiotemporal foundation models and generative AI capabilities for large-scale multimodal threat detection. The project aims to create new data representations and neural network architectures to scale up existing spatial detection algorithms, enabling the rapid deployment of analytical...
- This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
- This $250,000 Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to Carnegie Mellon University (CMU). The project aims to develop flexible, valid inference procedures for modern complex data that leverage powerful black-box machine learning algorithms. Key focus areas include novel variants of cross-validation to enable adaptive inference, as well as performance guarantees of cross-validation for...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $225,000 Project Grant to Carnegie Mellon University to develop a methodology for simulation-based inference that uses random features rather than carefully designed summary statistics. The 3-year grant, which runs from August 15, 2023 to July 31, 2026, aims to create a practical and generic tool for fitting simulation models to real-world data across diverse domains like astronomy, ecology, climate science, and...
- Carnegie Mellon University was awarded a $303,034 Project Grant from the National Science Foundation Division of Mathematical Sciences on August 1, 2021 to complete the project by June 30, 2024. The grant funds NEW APPROACHES TO QUESTIONS IN SAMPLING, COUNTING, AND OPTIMIZATION under the Mathematical and Physical Sciences program (CFDA 47.049). This program aims to promote progress in mathematical and physical sciences to strengthen the nation's scientific enterprise. The grant will support...
This $300,000 Project Grant was awarded on September 1, 2025 by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund research at Carnegie Mellon University to develop mathematically sound approaches for sampling and generative modeling in high-dimensional problems, which is critical for advancing machine learning and artificial intelligence (AI) techniques. The research aims to create efficient sampling methods with rigorous performance guarantees, focusing on two key tasks: sampling measures given by their density, and generating new samples based on example data. The project will investigate flow-based approaches to sampling and explore new geometries on the space of measures. The research will involve both graduate and undergraduate students, training a new generation of mathematicians with expertise in applied analysis and data science/AI. The ultimate completion date for the project is August 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 8/8/25 |