Project Grant 2413161
Award Date
9/1/24
Completion Date
8/31/27
Dollars Obligated
$599K
Federal Agency
Division of Information and Intelligent Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Austin, TX 78712, USA
Similar Awards
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 the University of California, San Diego (UCSD) to develop theoretical frameworks and computational methods for reconstructing complex 3D shapes using neural implicit representations. The key objectives are to enable the reconstruction of 3D shapes with intricate topologies, such as objects with holes, and to allow...
The National Science Foundation awarded a $800,000 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year award will support the development of a neurosymbolic program-synthesis framework that closely couples deep learning and classical symbolic methods for program synthesis. Researchers will explore new learning algorithms exposing neural models of code to explicit knowledge about program semantics....
This $299,964 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enhance computer vision capabilities for recognizing and understanding shapes in images and 3D data. The project will develop new deep learning and graph-based frameworks to enable computers to focus on the critical parts of an object, understand the relationships between those parts, and gain a more holistic perception of shapes....
This $399,161 National Science Foundation project grant supports research at the University of Texas at Austin to develop new algorithms and simulations for co-designing the geometry and fabrication plans for direct-ink writing 3D printing. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), this three-year award will transform additive manufacturing design tools by allowing users to specify an object's desired mechanical behavior and automatically generate...
This $305,850 federal Project Grant award was provided by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The project aims to advance the analysis and comparison of relaxed shape matching problems, investigate supervised and unsupervised deep learning approaches to improve the efficiency of manifold registration algorithms, and study extensions to account for partial or incomplete data and...
This project grant award of $600,000 from the National Science Foundation's (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA #47.070) program will support the development of hybrid models that combine deep neural networks and high-fidelity partial differential equation (PDE) solvers. The goal is to create a system that maintains the accuracy of PDE models while leveraging the speed of neural networks to enable accelerated solutions for...
This National Science Foundation (NSF) Project Grant under the Mathematical and Physical Sciences (CFDA# 47.049) program will fund a collaborative research effort between U.S. and U.K. investigators to develop new statistical methods for modeling dynamic changes in biological and other complex shapes. The $200,000 award, effective August 1, 2024 through July 31, 2027, will focus on integrating spatiotemporal registration of objects and their evolution into the statistical formulation to enable...
This National Science Foundation (NSF) Project Grant award, under the Mathematical and Physical Sciences (CFDA 47.049) program, will support research on stochastic methods and isoperimetric inequalities at Texas A&M University. The $238,406 award, active from July 2024 to June 2027, will develop techniques to bridge fundamental conjectures in Brunn-Minkowski theory and dual Brunn-Minkowski theory, with a focus on intersection bodies and higher-dimensional generalizations. The research aims...
This Project Grant from the National Science Foundation Division of Information and Intelligent Systems provides $469,704 to support research in developing deep learning-empowered nonlinear deformable models for computer graphics applications. Funded under the Computer and Information Science and Engineering program (CFDA #47.070), the award will support research at the University of Utah from October 1, 2022 to April 30, 2024. The project aims to develop new techniques integrating advanced...
The National Science Foundation awarded a $275,000 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year award will support the development of program synthesis techniques to help software developers manage schema changes to databases. The project aims to simplify the schema modification process through automated techniques for migrating data between formats and updating code to reflect schema...