Project Grant 2508145
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at North Carolina State University to explore advanced sampling and optimization techniques for decentralized machine learning. The key objectives are to: Enhance the sampling efficiency of interacting nonlinear Markov chains through adaptive spatio-temporal repellency among multiple "self-repellent random walks",...
- This National Science Foundation project grant of $487,371 will support research at the University of California, Santa Barbara from May 2022 through April 2025 under the Computer and Information Science and Engineering program. The grant will fund the development of new techniques for proving optimal convergence rates of Markov chain Monte Carlo algorithms. Specifically, the researchers will strengthen and extend the technique of spectral independence to establish optimal mixing time bounds for...
- This $157,975 federal Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research by The Pennsylvania State University (Penn State) on transport of particles in heterogeneous media and speeding up Markov Chain Monte Carlo algorithms. The first part of the project aims to model the effects of long-range correlations on ocean flow mixing rates, which is relevant to fields like computer science, materials science, and...
- This $875,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is developing a probabilistic programming framework for modeling hybrid systems that combine continuous state evolution and discrete state changes. The project is applying this framework to domains such as epidemiology, medical devices, and autonomous systems, with the goal of enabling rigorous model-based decision-making. Key project...
- 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 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program will support the development of a new Markov Chain Monte Carlo (MCMC) inference method called SHREK. The goal is to exploit the multi-fidelity nature of scientific and engineering models to guide the MCMC algorithm, allowing for faster convergence to the true highest-fidelity posterior with less total computation time. The $249,851 award, effective from June 15, 2025 to...
- 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...
- 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 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 Project Grant award for $313,927.00 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The award will fund research at Claremont McKenna College to study fundamental mathematics and algorithms for random sampling of structures on graphs, which has applications in areas like quantifying and detecting gerrymandering. The research aims to improve the speed and reliability of random sampling methods,...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $600,000 in funding to Purdue University to develop a new framework for discrete Markov Chain Monte Carlo (MCMC) algorithms that leverage gradients. The research aims to create faster, more scalable, and statistically reliable sampling algorithms to handle complex, discrete data more effectively in areas like AI, scientific simulations, drug design, recommendation systems, and natural language processing. The project will contribute open-source tools and train students and researchers to support the broader scientific community and build a skilled workforce, advancing science, innovation, and economic/societal well-being. The award term runs from August 1, 2025 to July 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 7/30/25 |