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 Project Grant award from the National Science Foundation Division of Information and Intelligent Systems provides $350,000 in funding to the University of Illinois from September 2022 through August 2026. The award supports research under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). Specifically, the University of Illinois will develop a machine learning framework for training models across hospitals to support precision population health and...
This $348,000 five-year federal Project Grant award is provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The award supports research to develop robust and ethical machine learning models for healthcare applications. Key objectives include: Improving model robustness to data errors and variations in patient populations and care settings through contrastive self-supervised deep metric learning. Enhancing...
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 $500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the acquisition of a GPU cluster at the University of Illinois Chicago (UIC) to accelerate HIPAA-compliant, data-driven research using artificial intelligence (AI) and machine learning. The system, which includes six mini-supercomputers and nearly 2 petabytes of storage, will enable UIC researchers to conduct transformative research...
This $250,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of algorithms for real-time dynamic risk identification and monitoring of streaming data, particularly in the domains of electronic medical records, mobile health, and supply chain. The key objectives are to create a unified framework for dynamic risk detection that can be incorporated into...
This $1.2 million project grant from the National Science Foundation's Engineering program (CFDA 47.041) will fund research at Stanford University from September 2022 to August 2026 to develop digital tools and algorithms to improve diabetes care delivery and health equity. Specifically, the university will leverage sensor data and machine learning techniques to better understand patient types and needs, allocate limited provider resources efficiently, and design interpretable treatment...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $1,183,690 to The Regents of the University of California, San Francisco (UCSF) to develop personalized machine learning models that predict adverse health events such as substance use and stress-related hypertension using data from consumer wearable devices like Fitbit and Apple Watch. The innovation of this 4-year project involves training self-supervised,...
The National Science Foundation (NSF) awarded a $169,982 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of Houston System. The grant, titled "COLLABORATIVE RESEARCH: CISE MSI: RDP: III: TOWARDS ROBUST AND HUMAN-ALIGNED DEEP LEARNING FOR MEDICAL-SENSOR TIME SERIES," aims to develop robust techniques for time-series deep learning models to address spurious correlations in medical sensor data applications. The key...
This Project Grant award, totaling $1,500,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The primary awardee, DePaul University, will design, develop, and evaluate an analytics dashboard to empower community health workers (CHWs) in underserved Chicago communities. This integrative research project aims to: 1) improve computational prediction modeling for emergency department...
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, explainable models to facilitate opportunistic screening, especially for underserved populations. Key technical objectives include integrating diverse data sources, extracting temporal relationships, addressing data biases, and improving model transparency. The research will also recruit and train underrepresented students in biomedical informatics. This work has the potential to significantly impact public health by enabling earlier intervention for chronic diseases.