Project Grant 2513689
Award Date
8/1/25
Completion Date
7/31/28
Dollars Obligated
$219K
Federal Agency
Division of Mathematical Sciences
Awardee
Lehigh University (E13MDBKHLDB5)
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Fountain Hill, PA 18015, USA
Similar Awards
This $162,576 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research at the University of California, Los Angeles (UCLA) to develop gradient-free optimization methods inspired by interacting particle systems for solving nonconvex optimization problems. The project aims to leverage collective intelligence principles to efficiently explore the landscape and converge to global minimizers for different types...
This $250,000 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development and analysis of a novel class of first-order optimization methods called Low-Rank Gradient Flow (LRGF). The goal is to advance techniques for solving challenging non-convex optimization problems that arise in a wide range of applications such as computer-aided design, radiation therapy, manufacturing, inverse problems, and artificial...
This National Science Foundation project grant of $250,000 will fund research at Rensselaer Polytechnic Institute from July 2022 to June 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports the development of accelerated distributed stochastic optimization methods and applications in machine learning. Specifically, the grantee will design fast-convergent and communication-efficient optimization algorithms with theoretical guarantees for solving...
This Project Grant award of $134,150 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research by The Trustees of the University of Pennsylvania, doing business as the Clinical Practices of the University of Pennsylvania, on nonconvex optimization algorithms for statistical estimation and machine learning. The overarching goal is to develop a mathematical foundation to explain the performance and applications of these nonconvex...
This $155,783 National Science Foundation project grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds collaborative research at Syracuse University to develop computationally efficient methods for non-smooth and non-convex optimization by exploring sparsity structures in large data sets. The research aims to address critical issues in non-smooth, non-convex optimization arising from sparse modeling of data for applications including machine learning and sparse...
This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $171,089 to Old Dominion University Research Foundation for collaborative research developing computationally efficient methods for non-smooth and non-convex optimization by exploring sparsity structures in large data sets. The research aims to address critical issues in non-smooth, non-convex optimization arising from sparse modeling of data used in machine learning and sparse...
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) to Rensselaer Polytechnic Institute (RPI) provides $220,000 to develop new stochastic algorithms for solving minimax-structured nonconvex and nonsmooth optimization problems. The research aims to improve the robustness of deep learning models against adversarial attacks, with applications in areas like game theory, statistics, engineering, and machine learning. Key deliverables...
This two-year, $251,672 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support research into non-smooth and non-Lipschitz Riemannian optimization methods. The grantee, Rice University, will study algorithmic approaches such as the manifold alternating direction method of multipliers and inertial manifold proximal gradient method to develop new tools for solving important classes of non-convex optimization...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $150,922 to Lehigh University to conduct research on asymptotic problems in kinetic theory. The project focuses on developing novel mathematical tools to characterize the multi-scale behaviors of particle systems in applications such as medical imaging, gas dynamics, and nuclear fusion. Key objectives include rigorous analysis to connect microscopic, mesoscopic,...
This $293,784 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports fundamental and applied research on fluctuating systems, random environments, and stochastic algorithms. The research aims to improve understanding and exploitation of randomness across diverse settings, including materials science, fluid dynamics, and machine learning. Key areas of focus include stochastic homogenization, stochastic partial...