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15,895 grants matching “artificial intelligence”
Hawaii Pacific Islands Mammography Registry
$650,989John Alan Shepherd · University Of Hawaii At Manoa · R01 · FY2025 · CA
Collaborative Research: FDT-BioTech: Digital Twins with Statistical Topological Learning of Periodontal Diseases
$650,892Yulia Gel · Virginia Polytechnic Institute And State University · · FY2025 · MPS
Project 1: Longitudinal Natural History Studies of Leukodystrophies
$650,777Adeline Lucie Vanderver · Children'S Hosp Of Philadelphia · U54 · FY2025 · NS
Defining the Neural Circuits of Attention Control: A New Hypothesis
$650,661Winrich Freiwald · Rockefeller University · R01 · FY2022 · MH
The laminar organization of 'index' versus 'attribute' coding in neocortex
$650,507Bruce L McNaughton · University Of California-Irvine · R01 · FY2024 · NS
The laminar organization of 'index' versus 'attribute' coding in neocortex
$650,507Bruce L McNaughton · University Of California-Irvine · R01 · FY2025 · NS
REmote symptom COllection to improVE postopeRative care (RECOVER)
$650,327Nawar Shara · Medstar Health Research Institute · R01 · FY2025 · MD
Integration of epidemiology, pathology, immunology and outcomes in colorectal cancer
$650,087Stephen B Gruber · Beckman Research Institute/City Of Hope · R01 · FY2023 · CA
A Multipronged Interrogation of Large-Scale Omics Data to Reveal COVID-19 Pathways
$650,002Carlos Cruchaga · Washington University · RF1 · FY2020 · AG
NSF Convergence Accelerator, Track K: Mapping the nation's wetlands for equitable water quality, monitoring, conservation, and policy development
$650,000Ludmila M Moskal · University Of Washington · · FY2024 · TIP
Integrating Data Science and Hands-on Experience into the Community College Biotechnology Classroom with Applications to Antibody Engineering
$650,000Dylan A Bulseco · Institute For Future Intelligence, Inc. · · FY2023 · EDU
**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE RESEARCH OBJECTIVE IS DESIGN, DEVELOPMENT, AND FIELD-TESTING OF AN ARTIFICIALLY INTELLIGENT METHOD TO PREDICT THE YIELD AND OIL CONTENT OF FLAX FROM A NUMBER OF MORPHOLOGICAL TRAITS BEFORE HARVESTING. SUCCESS IN THIS PROJECT WILL RESULT IN A QUANTUM LEAP FOR FLAX BREEDING PROGRAMS BY BRINGING IN A SYSTEMATIC DATA-DRIVEN AUTONOMOUS APPROACH, IN LIEU OF CONVENTIONAL HEURISTIC-BASED DECISION MAKING. NDSU HOSTS THE ONLY FLAX BREEDING PROGRAM IN THE US AND NORTH DAKOTA IS THE LARGEST PRODUCER OF FLAX (91% OF US PRODUCTION), WHICH WILL BE USED AS THE TESTBED IN THIS PROJECT. THE PROJECT REQUIRES PRECISION DATA COLLECTION ON MORPHOLOGICAL TRAITS THROUGHOUT THE ENTIRE LIFE CYCLE OF THE CROP. A COOPERATIVE TEAM OF UNMANNED AERIAL SYSTEMS (UASS) AND UNMANNED GROUND VEHICLES (UGVS) WILL BE EMPLOYED. THE UGVS WILL ALSO BE LOADED WITH A HYPERSPECTRAL CAMERA TO PREDICT THE OIL CONTENT EVEN BEFORE HARVESTING. THE SCHEDULING AND OPERATION OF THE UAS-UGV TEAM WILL BE DICTATED BY A DATA ANALYTICS ENGINE. THE VIDEOS COLLECTED BY THE UAS/UGV WILL BE PROCESSED TO EXTRACT VARIOUS MORPHOLOGICAL TRAITS AND TO PREDICT THE FINAL YIELD OF THE CROP THROUGH AN INTEGRATED MACHINE LEARNING MODEL. HYPERSPECTRAL IMAGES WILL BE ANALYZED USING MACHINE LEARNING TO PREDICT THE OIL CONTENT OF EACH PLOT. IF SUCCESSFUL, THIS PREDICTIVE MODEL FOR YIELD AND OIL CONTENT WILL ALLOW A BREEDER TO SUBSTANTIALLY LOWER THE COSTS OF THE BREEDING PROGRAM, AND HEREBY IMPROVE THE QUALITY OF THE NEW CROP VARIETY. IN COLLABORATION WITH AMERIFLAX, THIS FRAMEWORK WILL BE TESTED IN REAL-WORLD SETTINGS.
$650,000University Of California, Los Angeles · · FY2022 · National Institute of Food and Agriculture
NSF Convergence Accelerator Track K: Living Matter, Artificial Intelligence, and Water Nascency (LAWN) for Regenerative Environments and Equity
$650,000Maria Paz Gutierrez · University Of California-Berkeley · · FY2024 · TIP
**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** IN THE UNITED STATES, HORN FLIES (HF) ARE ESTIMATED TO CAUSE MORE THAN $1 BILLION IN ECONOMIC LOSSES ON PASTURED CATTLE ANNUALLY. ALTHOUGH STAGGERING, THESE NUMBERS MAY STILL BE AN UNDERESTIMATION OF THEIR FULL IMPACT. RELYING SOLELY ON INSECTICIDES HAS PROVEN TO BE INADEQUATE. IMPROVEMENT OF CATTLE RESISTANCE AND TOLERANCE TO HF THROUGH GENETIC TOOLS WAS NEVER CONSIDERED DESPITE THEIR REASONABLE GENETIC BASIS. THIS WAS THE RESULT OF THE LACK OF A COST-EFFECTIVE AND LOGISTICALLY EASY-TO-COLLECT TRAITS TO ASSESS HF ABUNDANCE UNDER PASTURE CONDITIONS, AND THE INABILITY OF ASSESS THE RESISTANCE AND TOLERANCE OF HF AT THE INDIVIDUAL LEVEL. IN THIS PROPOSAL, WE PRESENT TWO INNOVATIVE CONCEPTS TO ADDRESS BOTH PROBLEMS. FIRST, WE PROPOSED USING INDIRECT AND INTERMEDIATE PHENOTYPES THAT ARE ALREADY AVAILABLE OR COULD BE EASILY COLLECTED TO ASSESS HF ABUNDANCE. SENSOR DATA AND ARTIFICIAL INTELLIGENCE TOOLS WILL BE USED. SECOND, WE WILL DEVELOP CHANGEPOINT MODELS TO PREDICT, AT THE INDIVIDUAL LEVEL, THE ONSET OF ECONOMIC INJURY THRESHOLD (EIT) AND THE DECAY IN PERFORMANCE AFTER ONSET (DPO). JOINTLY, HF ABUNDANCE, EIT AND DPO WILL ALLOW THE DIRECT ASSESSMENT OF THE INDIVIDUAL RESISTANCE AND TOLERANCE TO HF. WE WILL DISSECT THE GENETIC BASIS OF THESE TRAITS AND WE WILL ASSESS THEIR ADEQUACY FOR GENETIC IMPROVEMENT OF RESISTANCE AND TOLERANCE TO HF.
$650,000University Of Georgia Research Foundation, Inc. · · FY2022 · National Institute of Food and Agriculture
NSF Convergence Accelerator Track K: COMPASS: Comprehensive Prediction, Assessment, and Equitable Solutions for Storm-Induced Contamination of Freshwater Systems
$650,000Jasim Imran · University Of South Carolina At Columbia · · FY2024 · TIP
Collaborative Research: FW-HTF-RL: Understanding the Ethics, Development, Design, and Integration of Interactive Artificial Intelligence Teammates in Future Mental Health Work
$650,000Saeed Abdullah · Pennsylvania State Univ University Park · · FY2023 · TIP
CAREER: Bio-inspired Nonequilibrium Design Principles of Molecular Information Machines
$650,000Zhiyue Lu · University Of North Carolina At Chapel Hill · · FY2022 · MPS
Major: CAIRA - A Creative Artificially-Intuitive and Reasoning Agent in the Context of Ensemble Music Improvisation
$650,000Jonas Braasch · Rensselaer Polytechnic Institute · · FY2010 · CSE
CRCNS Research Project: Multiply and Conquer: Replica-Mean-Field Limit for Neural Networks
$650,000Thibaud O Taillefumier · University Of Texas At Austin · · FY2021 · MPS
NSF Convergence Accelerator track L: Translating insect olfaction principles into practical and robust chemical sensing platforms
$650,000Baranidharan Raman · Washington University · · FY2024 · TIP
CAREER: Learning to learn - Artificial Intelligence Augmented Chemistry for Molecular Simulations and Beyond
$650,000Pratyush Tiwary · University Of Maryland, College Park · · FY2021 · MPS
CAREER: Leveraging physical properties of modern flash memory chips for resilient, secure, and energy-efficient edge storage systems
$650,000Biswajit Ray · Colorado State University · · FY2023 · CSE
ASCENT: Collaborative Research: Scaling Distributed AI Systems based on Universal Optical I/O
$650,000Vladimir M Stojanovic · University Of California-Berkeley · · FY2020 · ENG
NSF Convergence Accelerator Track L: Smartphone Time-Resolved Luminescence Imaging and Detection (STRIDE) for Point-of-Care Diagnostics
$650,000Xiaoshan Zhu · Board Of Regents, Nshe, Obo University Of Nevada, Reno · · FY2024 · TIP
NSF Convergence Accelerator Track L: Engineered microbial sensors for assessing water quality
$650,000Virginia W Cornish · Columbia University · · FY2024 · TIP