This Project Grant award from the National Science Foundation Division of Information and Intelligent Systems under CFDA 47.070 Computer and Information Science and Engineering provides funding of $548,346 from July 15, 2023 to June 30, 2028 to the University of Connecticut. The key products and services to be delivered under this award are: Development of deep learning frameworks trained on highly variable biological image data, including non-model organisms collected from the wild and imaged...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $345,135 to Saint Louis University to develop improved image retrieval systems for fine-grained visual categorization tasks. The project aims to better align image retrieval systems with human perceptions of visual similarity, enabling users to prioritize specific visual features most relevant to their domain-specific needs. Key...
This $400,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program will support collaborative research at the University of California, Berkeley aimed at developing a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives are to: 1) Design "white-box" deep neural networks that maximize information gain in the learned representations,...
The National Science Foundation (NSF) awarded a $169,618 Computer and Information Science and Engineering (CFDA 47.070) Project Grant to Oklahoma State University (OSU) to develop artificial intelligence (AI) and machine learning (ML) techniques that provide novel insights into the extinction risk of biological species. The project aims to leverage natural language processing and automated reasoning to address challenges in biodiversity data and species taxonomy classification, ultimately...
The National Science Foundation Division of Information and Intelligent Systems awarded a $1.2 million Project Grant to the International Computer Science Institute of Berkeley, California from October 1, 2021 to September 30, 2025. The grant supports research under the Computer and Information Science and Engineering program (CFDA 47.070) to develop scalable second-order methods for training, designing, and deploying machine learning models. The CFDA program aims to advance computing and...
This federal Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop transformative new wireless communication and artificial intelligence (AI) tools for automating biodiversity data collection, processing, and analysis. The $598,865 grant, awarded to Duke University, will fund the creation of probabilistic models to account for errors in inferring species composition from audio,...
The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $600,000 Project Grant to the University of California, San Diego (UCSD) under the Computer and Information Science and Engineering (CFDA 47.070) program. The 3-year grant, effective July 1, 2023, supports research to develop dynamic neural network architectures that can efficiently enable multimodal perception, including vision, audio, and language processing. The research aims to address...
This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
The National Science Foundation (NSF) awarded a $175,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of North Carolina at Charlotte. The project, titled "CRII: CSR: ENABLING ON-DEVICE CONTINUAL LEARNING THROUGH ENHANCING EFFICIENCY OF COMPUTING, MEMORY, AND DATA", aims to develop an efficient on-device continual learning framework that can incrementally learn new knowledge without forgetting prior learnt knowledge, while...