Project Grant 2316011
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a 3-year, $150,000 Project Grant to Duke University titled "COLLABORATIVE RESEARCH: COST-EFFICIENT AND CONFIDENT SAMPLING FOR MODERN SCIENTIFIC DISCOVERY" under the Mathematical and Physical Sciences (CFDA 47.049) program. The project develops cost-efficient sampling methods and supporting theory/algorithms to address challenges in modern scientific data analysis, such as Bayesian inference, efficient...
- 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...
- The National Science Foundation awarded a $250,000 project grant to Georgia Tech Research Corporation under the Mathematical and Physical Sciences program (CFDA 47.049) for the period of August 1, 2023 through July 31, 2026. The grant funds the development of experimental design-based weighted sampling techniques that introduce weights for each sample to improve upon existing sampling methods for quantifying population characteristics. The Principal Investigator will develop software packages to...
- 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 $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...
- The National Science Foundation awarded a 3-year, $300,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Regents of the University of California, Berkeley. The grant funds the development of a software infrastructure to facilitate the use of two cutting-edge Bayesian sampling methods - Preconditioned Optimized Convex Monte Carlo (POCOMC) and Microcanonical Hamiltonian Monte Carlo - by a range of scientists across disciplines such as astronomy, physics,...
- This $365,274 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of novel mathematical techniques and algorithms for designing cost-effective space-time sampling strategies and reconstruction methods for time-evolving functions on graphs. A diverse group of researchers from Northern Illinois University will work to analyze and manage various time-evolving processes sampled under realistic conditions and...
- This $170,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports fundamental research on experimental design and uncertainty quantification frameworks for complex systems. The research aims to develop new statistical surrogate models and sequential experimental algorithms to enhance the efficiency and effectiveness of information collection and decision-making for complex systems in...
- 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 $155,372 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will fund research to develop advanced statistical methods for extracting insights from high-dimensional, high-frequency "big data." The University of Illinois, Chicago, as the prime awardee, will focus on four key areas: 1) advancing contiguity theory to enable more robust statistical analysis of noisy, high-frequency data; 2) exploring time-varying...
This $350,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research to develop cost-efficient and confidence-building sampling methods for modern scientific discovery. The award to the Illinois Institute of Technology (IIT) aims to create a framework featuring new methodologies, theory, and algorithms that extend classical low-discrepancy sampling techniques. The goal is to enable more cost-effective Bayesian inference, efficient subsampling of massive datasets, multi-fidelity modeling, and improved density estimation. These capabilities will be demonstrated through ongoing collaborations studying heavy-ion collisions and real-time engine control of unmanned aircraft, as well as new partnerships that may develop over the 3-year project period from September 2023 to August 2026. The project will also produce an open-source Python library (QMCPy) to further strengthen these collaborative research efforts.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $350.0k | 7/24/23 |