This Project Grant award for $600,000, provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research at Brown University to develop new computational methods for integrating heterogeneous health data. The goal is to improve predictive modeling and explainable artificial intelligence techniques for advancing personalized healthcare and treatment. The key products of this 5-year project include novel graph-based...
This Project Grant award of $499,997.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a new framework to improve the interpretability and steerability of domain-specific AI models in medical imaging. The key products and services to be delivered include: Constructing an anatomically aware vision-language model capable of encoding and generating 3D medical images and radiology reports, to reduce the risk of...
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 $175,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to improve the efficiency and reliability of explainable AI (XAI) systems. The project aims to accelerate computationally intensive XAI algorithms, develop unified explainer models, and validate the methods in medical applications like histopathology imaging and cancer prognosis. This work will establish a...
This Project Grant award of $304,929.00 from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program supports the development of new artificial intelligence (AI) models that utilize causal understanding and reasoning. The project aims to create a methodology for developing AI models that can provide reliable, traceable, and human-comprehensible analytics and decision-making for healthcare applications. The goal is to overcome the limitations of...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a funding amount of $200,000, will support the development of concept-based reasoning approaches to improve interpretability and accountability of deep neural network (DNN) models for healthcare applications. The key research tasks under this 2-year award (10/1/2025 - 9/30/2027) are: (1) building inherently explainable concept-based DNN models for medical diagnosis, and (2)...
This $462,500 Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070), supports the development of advanced causal inference methods for data-driven decision making. Key products and services to be delivered include: Automated and robust causal AI systems that integrate machine learning and causal inference techniques to enable more decision-makers to leverage causal analysis. The project will...
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 $347,570 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research at Emory University to develop a comprehensive framework for knowledge graph-large language model (KG-LLM) co-learning in healthcare. The key objectives are to: 1) create novel methods for constructing comprehensive healthcare knowledge graphs by unifying existing sources, continuously improving them, and aligning them...
This $320,502 Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems is for a collaborative research project titled "Knowledge Discovery from Highly Heterogeneous, Sparse and Private Data in Biomedical Informatics." The research aims to mine healthcare data to identify patients likely to develop chronic conditions like type 2 diabetes and heart failure, and to develop models for opportunistic screening, particularly for...