Project Grant 2426762
Award Date
1/1/25
Completion Date
12/31/27
Dollars Obligated
$599K
Federal Agency
Division of Information and Intelligent Systems
Awardee
Duke University (TP7EK8DZV6N5)
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Durham, NC 27705, USA
Similar Awards
This $198,141 project grant awarded by the National Science Foundation's Biological Sciences program (CFDA 47.074) seeks to enhance predictive understanding of global species distributions through the development of advanced generative artificial intelligence (AI) models. The project aims to create a versatile foundation model that can be used by scientists, conservationists, and educators to better understand and protect the natural world. By training this model on a vast array of environmental...
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...
This National Science Foundation (NSF) Division of Environmental Biology Project Grant for $146,066 awarded to Furman University will leverage machine learning techniques to better understand biodiversity patterns measured through passive acoustic sampling. The key activities and deliverables include: Advancing the analysis of acoustic data from diverse ecosystems to address three lines of inquiry: i) how noise affects bird vocalizations and communications, ii) quantification of acoustic indices...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $120,000 to the Massachusetts Institute of Technology (MIT) will fund research to develop advanced computer vision approaches for global-scale biodiversity monitoring. The project, titled "CAREER: A COMPUTER VISION FOR GLOBAL BIODIVERSITY", aims to address key challenges in applying computer vision to process and extract insights from the vast...
This $297,323 Project Grant was awarded by the National Science Foundation (NSF) Division of Environmental Biology under the Biological Sciences (CFDA 47.074) federal grant program. The funding supports a collaborative research project to study how habitat structure, specifically the spatial arrangement of plants and resources, impacts avian biodiversity across local to continental scales. Using airborne and satellite Light Detection and Ranging (LiDAR) sensors, the project will explore the...
This Project Grant award from the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) provides $4,500,000 to The Ohio State University to establish the Global Center on AI and Biodiversity Change (ABC). The Center's objectives are to develop AI-enabled solutions for monitoring and understanding climate-induced biodiversity changes, and to support conservation decision-making and policy evaluation. Key focus areas include elucidating species boundaries,...
This Project Grant award from the National Science Foundation (NSF) Biological Sciences program (CFDA 47.074) provides $167,390 in funding to the University of Maryland, College Park to conduct collaborative research on linking remote sensing data with arthropod biodiversity analysis across an ecological gradient in the Hawaiian Islands. The key products and services to be delivered under this 3-year award include: Integrating rapid assessment technologies, such as satellite, helicopter,...
The National Science Foundation (NSF) awarded a $100,000 Project Grant under the Biological Sciences (CFDA 47.074) program to Cornell University. The award titled "COLLABORATIVE RESEARCH: ACED: PLANET-SCALE AI FOR ACCELERATING ENVIRONMENTAL SCIENCE - INVASIVE SPECIES AND BEYOND" will develop and validate an AI framework to automate and accelerate the generation of interpretable environmental science hypotheses at a planetary scale. The goal is to use a broad array of image data from...
The National Science Foundation's Division of Information and Intelligent Systems awarded a $174,827 Project Grant to the University of North Carolina at Charlotte to support research under the Computer and Information Science and Engineering program (CFDA No. 47.070). The award aims to develop deep learning frameworks trained on highly variable biological imaging data to enable accurate and scalable analysis of complex microscopic imagery. Key objectives include exploring deep learning model...
This National Science Foundation (NSF) Biological Sciences (CFDA 47.074) Project Grant award of $100,000 to The Trustees of Columbia University in the City of New York provides funding to develop and validate an AI framework that can use a broad array of image data collected from different sensing modalities (e.g., satellite, drone, internet-posted images) to automate and accelerate the generation of interpretable environmental scientific hypotheses at a planetary scale. The goal is to...