Project Grant 2239687

Award Date 9/1/23
Completion Date 8/31/28
Dollars Obligated $189K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Austin, TX 78712, USA
Similar Awards
This $113,018 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports the development of robust machine learning methods for imaging applications. The 5-year project at Michigan State University aims to advance techniques for image reconstruction and data correction using limited training data, with a focus on improving performance in the face of measurement artifacts, training-test variations, and distribution...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award provides $166,834 to the University of Maryland, College Park to develop a neural computational imaging framework capable of solving complex, high-dimensional computational imaging problems. The overarching goal is to create innovations in functional neural signal representations, neural forward operators, and self-supervised learning to enable breakthroughs in imaging...
The National Science Foundation Division of Computing and Communication Foundations awarded a $213,372 Project Grant to Rutgers, The State University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support the development of a theoretical framework for the design and analysis of snapshot compressive imaging systems to efficiently capture 3D data cubes such as hyperspectral images or video files from a single 2D measurement. The...
The National Science Foundation Office of Advanced Cyberinfrastructure awarded the University of Texas at Austin a $1.2 million Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) from September 1, 2022 to August 31, 2025. The grant funds research to develop a rigorous and reliable scientific deep learning framework for forward, inverse, and uncertainty quantification problems in computational science and engineering. Specific objectives include...
This $300,000 federal 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 perceiving and understanding shapes in images and 3D data. The key products and services to be delivered through this 3-year award include: Developing deep learning models and techniques to enable computers to focus on the critical parts that constitute an object and recognize the...
The National Science Foundation Division of Information and Intelligent Systems awarded a $154,231 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The two-year award will support research to develop a generative deep learning framework for approximating human decision-making processes on social networks when structural network data is unavailable. Specifically, the university researchers will...
The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Engineering (CFDA 47.041) program to The Research Foundation for the State University of New York (RF-SUNY) at the University at Albany. The grant, titled "CCSS: Uncertainty-Aware Computational Imaging in the Wild: A Bayesian Deep Learning Approach in the Latent Space," aims to develop advanced Bayesian deep learning techniques for computational imaging systems that can effectively handle various...
This Project Grant from the National Science Foundation Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $228,592 to support research at the University of Virginia from June 1, 2023 through May 31, 2028. The award will fund the development of new deep neural network models, algorithms, and applications to enable the analysis of geometric shapes in images. Specifically, the principal...
The National Science Foundation awarded a $586,319 Project Grant to the Texas A&M Engineering Experiment Station to support research and development under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year award will fund the development of a Bayesian paradigm for physics-informed machine learning algorithms. Key products include new probabilistic methods with quantified uncertainty, computational and analytical methods, and...
This five-year Project Grant from the National Science Foundation's Division of Information and Intelligent Systems and Computer and Information Science and Engineering program (CFDA 47.070) provides $330,293 to Texas A&M Engineering Experiment Station (doing business as Tees) to develop novel methods for sparse sampling and reconstruction in computer graphics rendering. The award seeks to dramatically reduce the number of photons required to generate high-quality images through...

The National Science Foundation Division of Computing and Communication Foundations awarded a $189,392 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The five-year award will support the development of a machine learning framework for robust computational imaging system design. Key products include new deep generative physical models to separate modeling of the physical imaging system from statistical prior knowledge learned from data. This aims to enable reliable imaging in dynamic clinical and research settings by reducing acquisition times, imaging moving objects, and compensating for system imperfections. The work will impact applications in microscopic, medical, and automotive imaging and also inform non-imaging system design where similar deployment challenges exist for deep learning algorithms. Outreach efforts include tutorials, webinars, and hands-on demonstrations to broaden participation in engineering and science.

Generated 1/7/24, 10:56 AM