Project Grant R01EB036016
- This $696,824 federal Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports research at the University of North Carolina at Chapel Hill (UNC-CH) to develop robust and interpretable multi-modal AI/ML models for precision medicine. The project aims to address key challenges in integrating complex, heterogeneous biomedical data...
- The federal Project Grant award of $221,224 was provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286). The award will fund research to develop AI language models with working memory constraints that better approximate those of humans, and conduct experiments with human participants to determine the extent to which these models can explain neural...
- 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 University of Pennsylvania received a $446,875 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286). The award will support research to develop deep learning tools that can predict patient risk, generate synthetic clinical biomarkers, and provide normative data from multimodal and longitudinal health data related to metabolic...
- This $300,000 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports the development of interpretable machine learning tools to uncover imaging biomarkers associated with breast and lung cancer. The project aims to create dimension reduction techniques to visualize features from deep learning models, establish an interpretable...
- This federal Project Grant award, titled "CAREER: TOWARD IMPROVING SCALABILITY FOR EFFECTIVE HEALTH LLMS," is funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070). The $348,227 award, effective September 1, 2025 through August 31, 2030, supports research to improve the effectiveness and scalability of large language models (LLMs) for healthcare applications. The key objectives are to develop evaluation...
- This EAGER (Early-concept Grants for Exploratory Research) Project Grant, awarded by the Division of Information and Intelligent Systems under the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), provides $200,000 in funding from October 1, 2025 to September 30, 2027. The project aims to develop "concept-based reasoning approaches" to improve the interpretability and accountability of deep neural networks (DNNs) and...
- The National Science Foundation (NSF) awarded a $499,997 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Trustees of Boston University. The grant, titled "CAREER: MAKING DOMAIN-SPECIFIC AI MODELS STEERABLE BY LEVERAGING FOUNDATIONAL MODELS," aims to develop new methods to make AI systems more transparent and adjustable for clinical applications. The project focuses on improving the fairness and reliability of medical AI by...
- This Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health (CFDA 93.286) program, provides $357,285 to develop an explainable artificial intelligence (XAI) based hybrid intrusion detection system to enhance the security of internet-connected medical devices. The research aims to create a formal threat analysis model, develop advanced machine learning...
- This $319,942 Project Grant awarded by the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) to the University of North Carolina at Chapel Hill (UNC-CH) focuses on building a collaborative and transparent open-source ecosystem to strengthen the reliability of artificial intelligence (AI) applications in healthcare. The project aims to enhance the safety, robustness, and interpretability of medical AI systems by developing shared infrastructure, governance...
This federal Project Grant award for $653,230.00, provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports the development of MED-SCALLOP - a novel neurosymbolic AI methodology and software tool. The goal is to create inherently explainable AI/ML models for clinical decision support in healthcare, which can effectively integrate deep neural networks with symbolic domain knowledge from medical experts. This will address challenges around the trustworthiness of AI in complex, noisy healthcare data analysis and treatment selection use cases. The award period runs from August 6, 2025 to May 31, 2029 and is being implemented by the University of Pennsylvania's Clinical Practices.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $653.2k | 8/6/25 |