Project Grant 2436945
- This $113,018 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research at Michigan State University (MSU) to develop robust machine learning methods for imaging applications. The project aims to advance supervised and unsupervised learning approaches that can effectively reconstruct and correct images using limited training data, while being resilient to perturbations such as...
- The National Science Foundation awarded a $200,000 Project Grant to the University of Chicago under the Engineering (47.041) federal grant program. The grant will support the creation of two open-source, high-performance computing-enabled deep learning frameworks to significantly improve the reconstruction speed and quality of full waveform inversion-based ultrasound computed tomography. One framework will incorporate adjoint tomography theory into a generative adversarial network to...
- This $162,510 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences will fund collaborative research at Tufts University to develop innovative, memory-efficient algorithms for high-dimensional imaging applications such as medical imaging, object inspection, and video surveillance. The key products will be accelerated reconstruction and compression techniques that can process large volumes of multi-dimensional imaging data in real-time while using...
- This federal Project Grant award of $180,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at Michigan State University to improve the robustness and trustworthiness of artificial intelligence (AI) models. The project aims to establish statistical frameworks for adversarial training in neural networks and develop scalable algorithms that leverage dynamic attack strategies and selective sampling to enhance the...
- This Project Grant award of $424,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) aims to develop new methods for quantum super-resolution imaging of passive, uncontrollable objects in real-time. The research team at the Stevens Institute of Technology will combine advanced physical models with artificial intelligence to overcome the fundamental diffraction limit that constrains optical imaging and sensing systems. This research seeks to...
- This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $220,000 to Yale University from September 1, 2023 through August 31, 2027. The project aims to develop a smarter artificial intelligence (AI) system to better understand and analyze complex medical images, such as those from multiple scans of a patient. The research team will tackle challenges to make the AI system more scalable, interpretable,...
- 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 $200,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support the development of analytic and numerical methods for emerging tomography techniques at North Carolina State University from August 1, 2022 to July 31, 2025. Specifically, the awardee will address mathematical problems arising from multi-energy computed tomography (MECT) and Compton camera imaging (CCI) to advance these promising medical imaging methods. For...
- This $1.2 million project grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), supports the development of novel model-based iterative reconstruction algorithms for high-resolution computed tomography imaging from low-dose X-ray data. The University of Florida will leverage techniques from approximation theory and performance optimization tools to address computational...
- This $558,594 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The grant supports research at the University of South Florida (USF) to develop new mathematical tools and a unified framework for studying the fundamental limits of computational imaging systems. The project aims to tackle deficiencies in existing techniques for analyzing the performance and jointly optimizing the...
The National Science Foundation (NSF) awarded a $400,000 Project Grant under CFDA 47.041 (Engineering) to Michigan State University (MSU) with a period of performance from September 1, 2025 to August 31, 2028. This research project aims to develop improved methods for tomographic imaging reconstruction and acquisition using novel combinations of conventional physical models and AI-based models. The key objective is to generate quantitatively accurate and reproducible images from current systems and future lower-cost systems, particularly in applications with limited or highly corrupted measurement data, such as medical imaging, materials science, and seismic imaging. The research seeks to significantly advance current algorithms for data acquisition, correction, and image reconstruction and analysis to enable high-quality object reconstructions in challenging tomography scenarios.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 8/19/25 |