This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) federal Project Grant award, with CFDA number 47.070, provides $182,477 in funding to Tufts University to develop "decision-aware" machine learning methods that can directly satisfy stakeholder goals in applications such as detecting heart disease and predicting opioid overdoses. The 5-year project, starting on July 1, 2024, will focus on three key technical innovations: 1) decision-aware...
This $666,667 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop innovative mathematical algorithms to enable safe automated patient monitoring, treatment guidance, and reconciliation of potentially conflicting medical treatments. The research will advance control theory, inference, and optimization to create new knowledge, leading to transformative approaches for coordinating complex interacting...
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 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $260,000 to The University of Texas Rio Grande Valley (UTRGV) to develop robust deep learning techniques for medical sensor time series data analysis. The key objectives are to: 1) identify input confounders that lead to spurious correlations in time series data, 2) design mitigation strategies to correct these spurious correlations, and 3)...
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 $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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will provide $198,000 to the Regents of the University of Minnesota to develop a trustworthy machine learning framework for augmenting clinical review of electroencephalograms (EEGs). The goal is to create reliable and scalable AI-based solutions to ease the burden of expert EEG review and reduce reviewer bias and errors in clinical decisions,...
The National Science Foundation (NSF) awarded a $593,662 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Maryland, College Park. The grant supports the development of an asynchronous distributed machine learning framework for collaborative analysis of brain imaging and genomics big data across multiple research sites. Key objectives include: 1) designing new asynchronous distributed algorithms for genome-wide association studies and...
This $170,000 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop robust and human-aligned deep learning techniques for analyzing medical sensor time-series data. The primary goals are to: 1) identify input confounders that lead to spurious correlations in time-series data, 2) design knowledge-editing strategies to correct these spurious correlations, and 3) investigate the techniques...
The National Science Foundation Division of Information and Intelligent Systems awarded a $600,000 Project Grant to the Massachusetts Institute of Technology from September 1, 2022 to August 31, 2026 under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support the development of machine learning-driven user interfaces and contextual displays to help clinicians synthesize information from patient medical records. Researchers will create a novel...