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...
This $317,591 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to enhance personalized healthcare through the use of large language models (LLMs) and novel memory semiconductor devices. The project aims to develop efficient retrieval-augmented generation (RAG) techniques for LLM personalization, focusing on reducing latency and hardware overhead through algorithm-hardware...
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 Division of Information and Intelligent Systems provides $625,000 to Duke University to develop an interpretable artificial intelligence framework for improving care of critically ill patients. The framework incorporates novel matching techniques known as Almost-Matching-Exactly to analyze observational data from patient treatment and emulate a randomized controlled trial. By matching each treated patient to similar untreated patients,...
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)...
The National Science Foundation (NSF) awarded a $350,000 Project Grant under the Biological Sciences program (CFDA 47.074) to the University of Illinois in Chicago. The grant funds collaborative research to develop novel machine learning and natural language processing methods for early identification of patients at risk of chronic conditions like type 2 diabetes and congestive heart failure. The project aims to mine heterogeneous healthcare data, protect patient privacy, and create fair,...
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 $302,293 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to improve self-experimentation technology to empower individuals in managing their health and wellbeing. The 5-year project, which commenced on June 1, 2025, seeks to enhance the agency of individuals to make decisions about their health by supporting explorations into personal health questions, such as the impacts of diet, physical activity, and...
This $1,183,690 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award to The Regents of the University of California, San Francisco (UCSF) supports the development of personalized artificial intelligence (AI) models that can predict recurring adverse health events, such as substance use and stress-related hypertension, using data from consumer wearable devices like Fitbit and Apple Watch. The key innovation of this project is the use...