Project Grant 2503118
- This federal Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $140,000.00 to develop a rigorous theoretical foundation for amortized inference, a novel paradigm in machine learning, statistics, and simulation. The research project aims to (1) deepen the understanding of the mathematical principles underlying amortized inference and (2) design improved methods with provable guarantees. Key objectives include...
- The National Science Foundation (NSF) awarded a $237,438 Project Grant to Purdue University under the Social, Behavioral, and Economic Sciences grant program (CFDA 47.075) to advance statistical inference on dynamic systems. The 3-year project will leverage deep learning and statistical modeling to enhance the efficiency, accuracy, and interpretability of time-series analysis across various domains. The research will introduce a new neural inference framework for estimating and inferring dynamic...
- 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 $250,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research by the University of Washington to explore the use of machine learning and artificial intelligence algorithms to augment limited datasets and improve statistical inference. The project will take a three-pronged approach: 1) establishing new semi-parametric efficiency results for semi-supervised learning, 2) developing new and improved...
- This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049), will develop foundational theory and insights to elucidate the mathematical, statistical, and contextual factors underlying memorization in artificial intelligence (AI) models. The $333,333 grant, awarded to Purdue University, aims to enhance the trustworthiness and applicability of AI by creating more reliable, robust, and privacy-preserving models that...
- This $245,190 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to advance the integration of modern machine learning tools, such as deep learning and Bayesian additive regression trees, into statistical modeling frameworks. The research program has two key objectives: Developing a novel Bayesian inferential framework for "generative models" - statistical models where data is viewed as stochastic outputs of...
- This federal Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), is focused on advancing the field of variational inference, a powerful technique used in machine learning and artificial intelligence algorithms. The primary objectives are to: Establish the theoretical foundations for a unified energetic variational inference framework to support and justify the use of existing and new variational inference algorithms...
- 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...
- This $140,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI) methods. The project, titled "Collaborative Research: Performance Guaranteed Statistical Learning with Multiple Classes of Models (Guided by PCS)," aims to advance a framework called...
- This Project Grant award of $160,000.00 from the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) grant program, will support a "Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning" project. The research aims to develop new statistical methods and frameworks for reliable uncertainty quantification in high-dimensional structure learning problems...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $210,000.00 in funding to Purdue University for the research project "Collaborative Research: Learning to Learn with Rigor: Foundations of Amortized Inference." The project aims to develop a rigorous theoretical foundation for amortized inference, which is a technique enabling efficient, real-time responses to statistical queries by learning a model-dependent mapping from data to distributions. This capability is crucial for modern advancements in generative AI, scientific machine learning, and simulation-based decision-making. The project comprises three interrelated thrusts: (1) investigating fundamental properties of mappings from data to distributions, (2) establishing statistical guarantees for the learned mappings, and (3) developing improved amortized inference methods with provable guarantees. The award period is from September 1, 2025 to August 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $210.0k | 8/25/25 |