Project Grant 2205418
- This four-year, $622,992 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop methods for making machine learning models more interpretable and reliable. Specifically, researchers at the University of Virginia will investigate the mathematical foundations of deep neural networks, with a focus on geometry and topology, to better understand internal representations. Computational tools will be designed based on these...
- The University of Utah was awarded a $499,384 project grant from the National Science Foundation to support research titled "AF: SMALL: THE GEOMETRY OF LEARNING ON STRUCTURED DATA OBJECTS" from October 1, 2021 to September 30, 2024. The grant was awarded under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in all areas of computing, communications, and information science and engineering....
- 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,...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $596,797 to the University of California, San Diego (UCSD) from September 1, 2024 to August 31, 2027. The project aims to develop automated frameworks for interpreting neural networks and designing robust, human-understandable neural network models. Key objectives include: (1) automating interpretations that describe the internal functioning of deep...
- This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) totaling $126,025 supports research by the University of Utah on robust manifold and metric learning techniques for handling noisy, high-dimensional data. The goal is to develop new mathematical tools and machine learning methods to better analyze and visualize complex data, such as genetic information or molecular images, by uncovering hidden geometric structures. The research aims...
- This National Science Foundation (NSF) Project Grant award, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $855,000 in funding to the Texas A&M Engineering Experiment Station (Tees) to develop a bimodal interpretable multi-instance medical image classification framework. The research aims to create a more scalable, interpretable, and robust artificial intelligence (AI) system to better analyze complex medical images, such as from multiple patient...
- This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
- This $500,000 Project Grant award from the National Science Foundation's Division of Mathematical Sciences supports the development of novel deep learning techniques for interpretable survival analysis of complex longitudinal healthcare data. The project aims to create a unified deep learning model that can effectively analyze multi-modal data, such as text, images, and lab values, collected at irregular intervals to predict patient outcomes. Key objectives include providing a unified feature...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $600,000 to The Pennsylvania State University from October 1, 2022 to September 30, 2025. The university will develop interpretable machine learning methods based on deep neural networks from a source coding perspective. Researchers will draw an analogy between explaining complex prediction models and transmitting signals with limited channel capacity. The...
- This $1,434,445.00 Project Grant award from the National Institutes of Health's Trans-NIH Research Support program (CFDA 93.310) supports the University of Southern California (USC) in developing new deep learning-based architectures, algorithms and training mechanisms to address key challenges in magnetic resonance imaging (MRI) reconstruction. The project aims to create a robust, reliable and trustworthy toolkit for reducing MRI acquisition time, enabling high-quality reconstruction with...
This Project Grant from the National Science Foundation Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $570,102 to the University of Utah from September 1, 2022 to August 31, 2026. The award will support the development of methods to improve the interpretability and reliability of deep learning models for medical imaging applications. Specifically, the University of Utah researchers will develop a mathematical and algorithmic foundation describing the geometry and topology of neural networks' internal representations. They will design efficient algorithms for computational analysis of these spaces to better understand model behaviors. Techniques will also be applied to enhance a model's interpretability by linking internal features to interpretable clinical concepts and through interactive visualization. Further, the project aims to leverage geometric and topological insights to identify and mitigate potential failures in deep learning models to improve reliability. The resulting techniques will be tested with clinical experts to aid in predicting patient outcomes for head and neck cancers.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $570.1k | 8/19/22 |