This Project Grant award of $325,000.00 was provided by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports collaborative research by a team at Emory University to develop acceleration and preconditioning methods for improving the training of large artificial intelligence (AI) models. The research aims to leverage numerical analysis and linear algebra techniques to...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
This $200,000 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support the development and theoretical study of robust acceleration algorithms with application to data-related sequences. A collaborative team from Emory University and the University of Minnesota will research strategies to improve the robustness of standard acceleration schemes for irregular sequences encountered in machine learning and data science applications....
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $296,023 to the Regents of the University of Minnesota to establish the mathematical foundations of two models that underpin generative artificial intelligence (AI) methodologies in scientific contexts: score-based generative models and transformer-based foundation models. The primary goals of this 5-year project are to: 1) study the role of fine data structures...
This Project Grant award of $450,000 from the National Science Foundation's Engineering program (CFDA 47.041) will support research on bi-level optimization for hierarchical machine learning problems. The award to the Regents of the University of Minnesota, conducting the work through their Office of Sponsored Projects Administration, aims to develop new approaches for modeling, analyzing, and innovating on a wide array of emerging machine learning applications using bi-level optimization...
This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) will provide $277,686 to the Regents of the University of Minnesota to develop advanced computational modeling and machine learning workflows for exploring the mechanical and electronic properties of 2D quantum materials. The project aims to enable rapid, automated, high-fidelity simulations of these materials, which are critical for advancing emerging...
This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $307,266 to North Carolina State University (NC State) to develop effective computational methods for training neural networks. The project aims to address fundamental challenges in artificial intelligence by creating a novel Exploration-Exploitation-Determination (EED) framework to significantly improve the training performance of neural networks, which are...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to advance artificial intelligence (AI) by investigating the mathematical foundations and practical applications of deep learning models. The $600,000 award, with a performance period from December 2024 to November 2027, will support research focused on understanding the properties of neural networks, the function spaces and data representations that emerge...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award, with CFDA Number 47.070, provides $214,364 in funding to the Regents of the University of Minnesota to advance the field of machine learning in chip design. The project aims to develop efficient and scalable computing paradigms for large graph machine learning on various electronic design automation (EDA) tasks, through algorithm-hardware co-design and optimization. The key...