The University of Memphis received a $190,960 two-year Project Grant from the National Science Foundation's Computer and Information Science and Engineering program to develop a novel machine learning framework for robust computational healthcare modeling with imbalanced health data. The framework will guide imbalance modeling by metadata such as demographics and location to incorporate varied imbalance patterns across these factors into model training. Key deliverables will include a...
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 $240,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new computational frameworks that merge large language models with neural operator learning techniques. The goal is to enable improved modeling of spatiotemporal phenomena in biomedical research, which could lead to advancements in personalized medicine, disease modeling, and drug discovery. Specifically, the research will focus on...
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 $1,000,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports the development of generative imaging models to verify and explain machine learning systems for healthcare applications. The key goals include: Developing robust "robustness audits" using synthetic data to assess how well a healthcare deep learning system will operate at different clinical sites, given variability in...
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...
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...
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 $706,868 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research and education initiative between Arizona State University (ASU) and the AI Institute for Foundations in Machine Learning (IFML). The project aims to develop robust, interactive, and embedded machine learning algorithms for deploying AI-enabled pervasive systems in real-world settings, such as healthcare monitoring and...
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...