Project Grant 2503119
- 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...
- 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...
- This $350,000 federal Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The goal of the research is to develop accurate mathematical models and computer simulations for studying non-equilibrium systems with memory effects, such as those found in biosystems, plasma evolution, and solid-state nanostructures. The Principal Investigator will focus on analytical and numerical approaches to statistical transport...
- This Project Grant award of $350,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research to develop new mathematical frameworks and solution algorithms for large-scale stochastic models across a range of application areas. The project will investigate geometric principles and strategies for effectively incorporating randomness into model representations and algorithm design, with a focus on solving equilibrium problems in...
- 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 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 $155,000 project grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) will develop new simulation-based inference (SBI) methods. These innovations aim to empower scientists to make better use of complex models across diverse domains such as genetics, ecology, biology, economics, and psychology, supporting more scalable, efficient, and reliable decision-making. The project will address two core challenges...
- This $300,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, awarded on April 1, 2025, aims to develop a foundation model for predicting rare events in atomistic simulations. The research, conducted by the University of Maryland, College Park, leverages advanced AI techniques like equivariant transformers, generative models, and multimodal learning to enhance prediction accuracy and generalization across...
- This Project Grant award, provided by the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences), aims to develop and analyze tools from applied harmonic analysis to advance more interpretable and explainable machine learning algorithms, as well as address complex optimization problems. The $270,000 award to the University of Maryland, College Park, will pursue two main research thrusts over a 3-year period from September 1, 2025 to August 31, 2028. The first thrust...
- The National Science Foundation (NSF) awarded a $600,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to The Trustees of the University of Pennsylvania, doing business as Clinical Practices of the University of Pennsylvania. The grant funds a 3-year research project to develop a new non-asymptotic statistical foundation for Approximate Message Passing (AMP) algorithms, which are widely used to solve linear regression problems. The...
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 investigating functional and statistical guarantees for the learned data-to-distribution mappings, and developing methodological advancements. The research is being conducted at the University of Maryland, College Park, a prominent research institution with extensive expertise in areas such as quantum computing, artificial intelligence, and national security. This collaborative research project seeks to advance the state-of-the-art in amortized inference, with potential applications in generative AI, scientific machine learning, and simulation-based decision-making.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $140.0k | 8/25/25 |